With the rapid development of low-carbon buildings and structural-energy storage integration technologies, cement-based materials, as core components of building structures, are increasingly required to possess simultaneously load-bearing and energy storage functions. However, the inherently low ion transport efficiency of traditional cement-based materials, along with the difficulty in simultaneously improving mechanical and energy storage performance, limits their application in structural energy storage. Steel slag, a bulk solid waste with hydration activity, can be utilized as a supplementary cementitious material to regulate pore structure and optimize microstructural interfaces, thereby enhancing ionic transport and energy storage performance. Meanwhile, electrolyte type plays a crucial role in the electrochemical performance of cement-based energy storage devices. Therefore, a systematic investigation into the effects of steel slag on the mechanical and energy storage properties of cement-based materials in different electrolyte systems is of considerable theoretical value and practical significance.

Porous cement-based structural electrolytes were prepared using aluminum powder as a pore-forming agent and steel slag as a supplementary cementitious admixture, with a water-to-binder ratio of 0.4. Aluminum powder contents were 0.02%, 0.05%, and 0.10%, while steel slag contents were 0%, 15%, 30%, and 45% (by mass of cement). After standard curing for 28 d, specimens were immersed in 2 mol/L KOH or ZnSO₄solution for 7 d. Activated carbon (AC), acetylene black, and polyvinylidene fluoride were mixed, and N-methylpyrrolidone was added before grinding to form a uniform slurry, which was coated onto a stainless-steel foil current collector and dried to obtain AC electrodes. Subsequently, AC//AC symmetric supercapacitors and Zn//AC hybrid supercapacitors were assembled in the KOH system and ZnSO₄ system, respectively. The compressive strength of the samples was measured, and ionic conductivity was evaluated using an electrochemical workstation. The microstructure and phase composition were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray computed tomography (X-CT). The electrochemical performance of the supercapacitors was assessed using electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic charge-discharge measurements.

The synergistic effect of aluminum powder-induced pore formation and steel slag incorporation significantly promoted the development of an interconnected pore network, thereby increasing ionic conductivity. Ionic conductivity increased with increasing aluminum powder and steel slag contents, reaching 130.5 mS/cm (KOH) and 26.1 mS/cm (ZnSO₄). The type of electrolyte had a significant impact on the compressive strength of the samples. After immersion in KOH solution, the compressive strength of the samples remained essentially unchanged, indicating that KOH mainly contributed to pore-solution filling. In contrast, after immersion in ZnSO₄ solution, the compressive strength of the samples increased markedly. The compressive strength of the S1A5 specimen increased from 9.6 MPa to 17.5 MPa, corresponding to an improvement of 82.3%. Microstructural analysis revealed that ZnSO₄ reacted with cement hydration products to form compounds such as zinc hydroxide sulfate and gypsum, which effectively filled internal pores and refined the pore structure, thereby enabling the simultaneous enhancement of ionic conductivity and compressive strength.

Electrochemical measurements demonstrated that the devices based on the KOH system exhibited a typical electric-double-layer capacitive behavior, characterized by low internal resistance and excellent rate capability, but had a relatively low energy density of only 10.8 W·h/kg. In contrast, the ZnSO4-based devices displayed a hybrid energy-storage mechanism combining electric double-layer capacitance and pseudocapacitance, resulting in a significantly improved energy density of 72.2 W·h/kg. Both types of devices exhibited excellent cycling stability, with capacity retention exceeding 90% after 1000 charge-discharge cycles.

This study demonstrates that aluminum powder and steel slag synergistically regulate the pore structure and ionic transport properties of cement-based structural electrolytes. The ionic conductivity of the KOH system is markedly higher than that of the ZnSO₄ system. ZnSO₄ can react with cement hydration products to enhance compressive strength and ionic conductivity simultaneously, while also enabling high energy density in Zn//AC hybrid supercapacitors. Steel slag contributes to multi-performance optimization by promoting pore connectivity, facilitating ion transport, and improving interfacial electrochemical processes. These findings highlight the dual value of solid-waste reuse and functional structural regulation, and provide important guidance for the design and application of cement-based structural energy storage devices.

Hydration heat prediction is important for mixture design, thermal control, and early-age cracking risk assessment for cement-based materials. The increasing use of supplementary cementitious materials (SCMs) and chemical admixtures in low-carbon concrete has made hydration behavior more complex, while conventional empirical or simplified analytical models have difficulty describing multicomponent heat release. Therefore, this study develops a general model for predicting cumulative hydration heat directly from mixture composition.

A database of 219 cumulative hydration heat curves and 13 048 data points was compiled from 33 published isothermal calorimetry studies, covering ordinary Portland cement and blends with fly ash, ground granulated blast-furnace slag, and silica fume. The input features included water-to-binder ratio, component fractions, oxide compositions, specific surface area, and loss on ignition. Hydration kinetic equations, including the Schindler-Folliard, Krstulovic-Dabic, and Knudsen equations, were embedded into neural networks as physical priors. The framework consisted of a physical-parameter prediction branch, a physical-equation layer, and a residual-correction branch, enabling end-to-end physics-driven prediction with data correction. Ten model variants were trained and compared under identical experimental settings.

SFQmaxDecompNet achieved the best overall performance, with a test R² of 0.962, a generalization gap of 0.018, and 94% of test curves reaching R²≥0.9. Its component-wise decomposition of Q_max better represented the different heat-release potentials of cement, FA, GGBS, and SF. KD-B ranked second (R² = 0.937), benefiting from its two-peak representation of early cement hydration and delayed SCM reactions. In contrast, KD-C had seven physical parameters but reached only R² = 0.868, showing that model performance depended more on the consistency between physical assumptions and data characteristics than on the number of parameters alone. PureNN reached R² = 0.874, lower than several physics-embedded models.

Data-efficiency tests showed that physical priors reduced the dependence on training data. When only 30% of the training curves (45 curves) were used, SFQmaxDecompNet still achieved R²=0.571, whereas PureNN degraded nearly completely. The physics branch dominated under limited data, while the residual branch provided local corrections when data were sufficient. Interpretability analyses further showed that Q_max represented the total heat potential, τ reflected the hydration time scale, and β described the concentration of heat release. Permutation importance and ALE analyses identified cement content as the dominant output feature, followed by FA, GGBS, FA-CaO, W/B, and SF. The temporal ALE trends of Q and dQ/dt were consistent with the delayed reactions of FA and GGBS and the early promoting effect of SF.

The proposed physics-driven and data-correction synergistic neural network provides accurate and interpretable hydration heat prediction of composite cementitious materials. Embedding hydration kinetic equations as inductive biases improves generalization, suppresses non-physical oscillations, and enhances robustness under small-sample conditions. This framework provides a useful route for mixture design, thermal analysis, and early-age crack-risk control in low-carbon cement-based materials.

Ultra-high-performance concrete-filled steel tubular (UHP-CFST) members exhibit excellent overall performance; however, the opaque outer steel tube completely prevents direct observation of the damage evolution within the UHPC core. With the continuing development of deep and distant offshore regions, engineering structures are increasingly exposed to extreme loading conditions, which can cause irreversible damage to the core concrete of CFST members. Therefore, the introduction of structural health monitoring (SHM) techniques for accurate identification of structural damage severity is particularly important for these members. Acoustic emission (AE) technology, owing to its in situ passive monitoring capability and sensitivity to microscopic defects, is well suited to such harsh conditions and has become an ideal nondestructive evaluation tool for this type of member. However, although AE enables dynamic monitoring, conventional parameter-based analysis methods, such as the Ib-value and hit count, still rely heavily on manual experience and have limited capability in representing the complex nonlinear relationships embedded in AE signals, which makes it difficult to fully extract the latent damage information contained in high-dimensional AE data. In recent years, deep learning (DL) has been widely applied in pattern recognition, image classification, and regression tasks. Compared with conventional methods, DL methods can automatically extract high-dimensional features and establish nonlinear mappings among complex data, and have therefore attracted increasing attention in the field of structural health monitoring. Against this background, axial compression tests were conducted on 27 UHP-CFST short columns with synchronous AE data acquisition, and an intelligent classification framework was developed to identify damage stages in the specimens.

A total of 27 UHP-CFST short columns with an outer diameter of 152 mm and a height of 456 mm were tested under concentric axial compression. The test matrix included three steel grades (Q235, Q355, and Q420) and three wall thicknesses (4, 6 mm, and 8 mm), and all specimens were filled with UHPC and three replicate specimens were prepared for each group. The tests were conducted under displacement control, with a loading rate of 1.0 mm/min in the initial stage and 2.0 mm/min after the peak load was reached. The applied load and axial displacement were recorded synchronously by the built-in data acquisition module of the testing machine and external high-precision linear variable differential transformers (LVDTs), respectively. Meanwhile, a 16-channel MISTRAS acoustic emission monitoring system developed by Physical Acoustics Corporation (PAC), USA, was used to monitor crack development in the UHPC core throughout the test.

Based on the evolution of normalized cumulative hits (NCH) as a function of load percentage, the damage process was divided into three stages. AE feature parameters were subsequently extracted, and minority-class data were augmented using a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to mitigate class imbalance. Based on the augmented data, a damage-stage identification model based on a one-dimensional residual network (1D-ResNet) was further established to classify different damage stages intelligently.

The AE samples generated by WGAN-GP exhibited high similarity to the original samples in terms of both statistical characteristics and feature-space distribution. For Stage I damage identification, the developed ResNet model achieved a precision of 88.4%, a recall of 95.0%, and an F1-score of 91.6%. These results indicated that the model was able to identify most Stage I samples, with relatively few missed detections. The high recall suggested that the model was highly sensitive to the initial damage stage and could effectively capture early damage features. However, the relatively lower precision indicated that some Stage II samples were misclassified as Stage I, implying the presence of a limited number of false positives. For Stage II damage identification, the model achieved a precision of 92.7%, a recall of 95.0%, and an F1-score of 93.8%. All three metrics remained at a high level, and the precision and recall were relatively well balanced. These results indicated that the model was able to effectively extract the AE characteristics of Stage II and accurately distinguish them from those of the other stages, thereby yielding the best overall identification performance. For Stage III damage identification, the model achieved a precision of 100%, a recall of 90.0%, and an F1-score of 94.7%. These results indicated that the model exhibited extremely high identification accuracy for Stage III. When a sample was classified as belonging to Stage III, no misclassification occurred, indicating the absence of false positives. However, some true Stage III samples were assigned to other stages, which led to a slightly lower recall. The confusion matrix further showed that the model exhibited strong identification capability for all three damage stages. Among them, the classification performance for Stages II and III was particularly good. A small number of misclassifications were observed between Stages I and II, which could be attributed mainly to the continuity of the damage evolution process, as some late Stage I samples and early Stage II samples exhibited a certain degree of overlap in the AE feature space.

In addition, the comparison of identification results among different models showed that the ResNet model achieved high classification accuracy across all three damage stages, with overall performance markedly superior to that of the SVM, MLP, and CNN models. Overall, the results demonstrated that the ResNet model constructed on the basis of the augmented dataset could effectively learn the damage evolution patterns embedded in the AE features and achieve accurate identification of different damage stages in UHP-CFST short columns under axial compression.

The data generated by WGAN-GP show high consistency with the original data in terms of both statistical characteristics and the distribution in high-dimensional feature space, indicating that the overall distribution trend and the main feature structure of the AE data are well preserved. On this basis, a three-stage damage identification model based on ResNet is established and further compared with support vector machine (SVM), multilayer perceptron (MLP), and convolutional neural network (CNN) models. The results show that the F1-scores of the ResNet model for Stages I, II, and III reach 91.6%, 93.8%, and 94.7%, respectively, demonstrating overall identification performance superior to that of the SVM, MLP, and CNN models. Therefore, the proposed method combining WGAN-GP and ResNet effectively improves the intelligent identification of axial compression damage stages in UHP-CFST short columns and provides a new perspective for AE-based structural damage identification.

Faced with worsening environmental problems and fossil energy depletion, renewable energy has become a focus of the global energy transition, whereas its intermittency and variability require large-scale energy storage systems. Commercial lithium-ion batteries are limited by safety hazards and high costs, making novel, safe, and low-cost energy storage systems critical. Thus, building-integrated structural energy storage systems have emerged, in which the separator, a core component, must perform multiple functions. Cement-based materials, because of their abundant raw-material sources and excellent stability, are ideal for separators and have been applied in supercapacitors and zinc-based batteries. Aqueous zinc-iodine batteries, featuring intrinsic safety, low cost, and good redox reversibility, are suitable for structural energy storage systems. The liquid active material system (iodide dissolved in electrolyte) avoids the drawbacks of solid iodine cathodes, and its combination with porous cement separators can meet building operation requirements (low maintenance, long-term stability). As a core factor regulating battery performance, electrolyte concentration determines active substance content and ion conduction. Thus, clarifying its effect on cement-based separators and zinc-iodine batteries and optimizing electrolyte formulation are essential. This study integrates foamed cement paste separators (FCPS) into zinc-iodine batteries. By regulating the cement paste pore structure through foaming and adjusting the zinc iodide concentration, this study aims to optimize ion conduction and mechanical and electrochemical properties. The study explores the influence of zinc iodide concentration on the physicochemical properties of FCPS and battery performance, and determines the optimal electrolyte formula, aiming to provide theoretical and experimental support for high-performance cement-based zinc-iodine batteries and building-integrated structural energy storage systems.

FCPS specimens were prepared by a foaming technique with a water-cement ratio of 0.5. After incorporating 0.1% aluminum powder by cement mass, the mixture was poured into molds for curing. For battery assembly, active carbon (AC), acetylene black, and polyvinylidene difluoride (PVDF) were mixed at a mass ratio of 8:1:1, blended with N-methylpyrrolidone (NMP) to fabricate AC cathodes with a loading of 2.48 mg·cm^-2. The electrolyte consisted of 2 mol/L zinc sulfate (ZnSO₄) and 0 to 0.20 mol/L zinc iodide (ZnI₂). FCPS were vacuum-soaked in the electrolyte for 7 d, and the zinc-iodine batteries were assembled with a cathode-separator-anode configuration. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to characterize the phase and microstructure of FCPS. Compressive strength before and after electrolyte immersion was measured by a UTM5105 tester, and pore structure was analyzed by mercury intrusion porosimetry (MIP). Electrochemical behaviors including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and galvanostatic chargedischarge (GCD) were tested to evaluate battery performance, and ionic conductivity, areal capacity, areal energy density, and areal power density were calculated.

The pristine FCPS presented a complex phase composition, consisting of unhydrated cement clinker minerals and various hydration products. After immersion in the electrolyte, the diffraction peak intensity of calcium hydroxide decreased, whereas characteristic peaks of zinc sulfate hydroxide hydrate (Zn₄(OH)₆SO₄⋅4H₂O, ZSH) and gypsum appeared, indicating chemical reactions between cement hydration products and the electrolyte. Pore-structure analysis revealed that the original FCPS had a porosity of 44%. After electrolyte soaking, the porosity decreased with obvious pore refinement, reduced pore connectivity and increased tortuosity. A porosity of 33% was obtained at 0.15 mol·L^-1 ZnI₂, yet the FCPS still maintained stable ion transport performance. The ionic conductivity of FCPS first increased and then decreased slightly as the ZnI₂ concentration increased, reaching a maximum of 10.20 mS·cm^-1 at 0.15 mol·L^-1, which was predominantly governed by the intrinsic conductivity of the electrolyte. The compressive strength of FCPS was substantially enhanced after electrolyte treatment and reached its maximum (7.54 MPa) at 0.15 mol·L^-1 ZnI₂, closely associated with pore filling and porosity evolution.

Electrochemical tests of FCPS-based zinc-iodine batteries indicated that increasing the ZnI₂ concentration reduced the solution resistance, with the lowest value obtained at 0.15 mol·L^-1. CV results confirmed the highly reversible I₂/I^- redox reaction, and the peak current increased with higher ZnI₂ concentration. GCD and rate-performance results showed that a moderate ZnI₂ concentration extended the charge-discharge plateau and improved areal capacity. Under the optimal electrolyte formula (2 mol·L^-1 ZnSO₄ and 0.15 mol·L^-1 ZnI₂), the battery delivered an areal energy density of 2.00 mW·h·cm^-2 at a power density of 0.25 mW·cm^-2, and retained 82% of its initial capacity after 1000 cycles, outperforming most previously reported cement-based batteries. Regarding the energy-storage mechanism, the multi-scale pore structure of FCPS provided sufficient electrolyte storage space and ion transport pathways. The in situ formation of ZSH and gypsum balanced mechanical stability and electrochemical performance. The optimized ZnI₂ concentration ensured high ionic conductivity and sufficient active iodine sources. The synergistic effect of these three factors enabled the integration of structural load-bearing and electrochemical energy storage for building applications.

In this study, a safe and environmentally friendly zinc-iodine battery is integrated with foamed cement paste to develop a novel energy storage system that integrates structural load-bearing capacity, electrochemical functionality, and large-scale application potential. A porous cement paste separator (FCPS) is fabricated via a foaming technique, enabling it to serve dual functions: acting as a battery separator that facilitates ion conduction and electrolyte retention while providing sufficient load-bearing capacity to maintain structural integrity. A zinc sulfate solution is used as the base electrolyte, into which different concentrations of zinc iodide are introduced to regulate the content of iodine active species and the ionic transport environment. The results demonstrate that the synergy between the porous structure and the optimal zinc iodide concentration is critical to enhancing both the ionic conductivity of the separator and the electrochemical performance of the battery. The foaming technique endows FCPS with abundant pore structures, which construct continuously connected ion transport channels and provide sufficient storage space for the electrolyte. Meanwhile, the optimal zinc iodide concentration (0.15 mol·L^-1) improves the ionic conductivity of the electrolyte and, as an active iodine source, increases the number of iodide ions participating in the I₂/I^- redox reaction. This synergy enables the foamed cement paste separator to achieve a porosity of 33%, an ionic conductivity of 10.20 mS·cm^-1, and a compressive strength of 7.54 MPa. Based on this optimal zinc iodide concentration, the assembled zinc-iodine battery delivers a high areal capacity of 1.60 mA·h·cm^-2 and an energy density of 2.00 mW·h·cm^-2 at a power density of 0.25 mW·cm^-2, with a capacity retention of 82% after 1000 cycles. This study provides a new strategy for large-scale energy storage and demonstrates the promising application of zinc-iodine batteries in building structural energy storage.

Accurate prediction of concrete carbonation depth is essential for durability assessment and service-life prediction of reinforced concrete structures, as well as for evaluating the CO₂ uptake capacity of cement-based materials. Concrete carbonation is affected by material composition, mix proportions, curing conditions, and the exposure environment, and these factors exhibit complex nonlinear coupling effects. Traditional empirical models are generally based on simplified assumptions and limited variables, making it difficult to fully describe the carbonation process. Machine learning (ML) models can capture nonlinear relationships from large datasets and achieve high prediction accuracy, but they usually lack explicit analytical expressions. To balance prediction accuracy, model simplicity, and interpretability, this study proposes an interpretable ML-based framework for constructing an explicit prediction formula for concrete carbonation depth.

A natural carbonation database shared by Marani et al. was used in this study. After data cleaning and feature processing, a database containing 35 input features, 2077 carbonation-depth data points, and 709 concrete mixtures was obtained. Decision tree (DT), random forest (RF), XGBoost (XGB), and CatBoost (CB) models were established for carbonation depth prediction. Considering the skewed distribution of carbonation depth, log1p transformation was applied to the target variable. The data were divided by concrete mixture group to avoid data leakage. SHAP analysis was then used to rank feature importance and guide feature selection. Based on the optimal CB model, the relationships between input variables and their SHAP contributions were fitted, and the additive SHAP contribution terms were further transformed into a multiplicative explicit prediction formula.

The carbonation depth data showed an obvious skewed distribution, and log1p transformation improved model training stability and test-set prediction performance. By comparing different ML algorithms and feature sets, the CB model based on log1p transformation and the FS2 feature set showed the best overall performance. The root mean square error (RMSE) values of the training and test sets were 0.55 mm and 1.14 mm, respectively, and the corresponding coefficient of determination (R²) values were 0.98 and 0.93. These results indicated that the model could accurately predict natural carbonation depth and effectively capture the nonlinear relationship between input variables and carbonation depth. In the final FS2-CB model, SHAP analysis showed that W/CaO, carbonation time, and carbonation environment were the three most important variables affecting the prediction of carbonation depth. Among them, W/CaO showed the highest feature importance, indicating that it was a key combined variable influencing model prediction. Based on the SHAP dependence relationships, variable contribution functions were fitted, thereby enabling transformation from a black-box model to an explicit prediction formula (PF).

The PF model achieved an RMSE of 2.28 mm on the test set, which was approximately 47.2% lower than that of F4, the best-performing formula model on the same test set. This indicated that the PF model had higher prediction accuracy than traditional formula models on the original dataset. On the external dataset, the PF model achieved an RMSE of 4.48 mm, which was 11.6% lower than that of the ML model (5.07 mm), and also lower than those of formula models F1-F3, which ranged from 5.91 mm to 8.52 mm. These results showed that although the PF model was less accurate than the optimal ML model on the original full dataset, it combined relatively high prediction accuracy, an explicit mathematical expression, and better cross-source generalization ability, indicating its potential for engineering application.

This study proposes an interpretable machine learning-based method for concrete carbonation depth prediction and explicit formula extraction. Through literature database processing, feature selection, machine learning modeling, and SHAP contribution function fitting, a high-accuracy black-box model is transformed into an interpretable formula model. The CB model with log1p transformation and the FS2 feature set achieves the best prediction performance. In the final FS2-CB model, SHAP analysis shows that W/CaO, carbonation time, and carbonation environment are the dominant variables controlling carbonation depth, indicating that concrete carbonation is mainly governed by pore transport conditions, carbonation buffering capacity, exposure time, and environmental effects. The constructed PF model shows higher accuracy than traditional formula models and better cross-source generalization performance than the ML model on the external dataset. Overall, the proposed method improves model simplicity and interpretability while maintaining relatively high prediction accuracy, providing a useful reference for explicit model construction in concrete carbonation depth prediction and other performance prediction tasks of cement-based materials.

With its carbon neutrality target fast approaching, China's cement industry urgently needs to develop functional cement-based materials with high carbon sequestration capacity. low-calcium carbonatable binders (LCCB) show great potential for carbon reduction because of their low calcium-to-silica ratio and low calcination temperature; but their poor carbonation performance limits their carbon-reduction applications. To address this limitation, we systematically investigated the effects of barium (Ba) ion doping and magnesium nitrate (Mg(NO₃)₂) solution on the carbonation performance of LCCB by combining first-principles calculations with experimental tests. The results indicate that Ba ion doping increases the volume of the substitution polyhedra in wollastonite (CS) and rankinite (C₃S₂), providing a thermodynamic driving force for the dissociation of calcium ions during the early stages of carbonation. When the concentration of Mg(NO₃)₂ solution is 0.1 g/mL and the Ba ion doping level is 1% (molar fraction) of the calcium ions in the raw material, the initial reaction rate and carbonation activity of LCCB after 8 h of carbonation show significant improvement. In this case, the compressive strength and carbon sequestration rate are 101.7 MPa and 18.33%, respectively, representing increases of 53.63% and 55.34%. Analyses of carbonation products and microstructure indicate that Ba ion doping acts synergistically with Mg(NO₃)₂ to promote the rapid formation of CaCO₃ and amorphous silica gel, reduce porosity, and optimize the pore structure. These findings show that a synergistic modification strategy can effectively improve the carbonation performance of LCCB.

The mechanism by which Ba ions and Mg(NO₃)₂ synergistically enhanced the carbonation performance of LCCB was elucidated by combining first-principles calculations with experiments. The energy barrier for calcium ion dissolution decreased, and the synergy between Ba ions and Mg(NO₃)₂ also accelerated the early-stage carbonation rate of LCCB, creating favorable conditions for improving the carbon sequestration efficiency and compressive strength. Moreover, the marked increase in carbonation products effectively filled the larger pores, and the presence of aragonite further promoted the carbonation process. Thus, the synergy between Ba ions and Mg(NO₃)₂ effectively improved the carbonation performance of LCCB and provided an effective strategy for developing functional cement-based materials with high carbon dioxide sequestration capacity.

Ba ion doping and Mg(NO₃)₂ act synergistically to markedly improve the carbon sequestration rate and carbonation hardening performance of LCCB. During the early stage of carbonation, Mg(NO₃)₂ and Ba ions work together to increase the carbonation reaction rate and reactivity of LCCB, thereby speeding up the carbonation process. When the Mg(NO₃)₂ content reaches 1%, its synergistic effect with Ba ions is optimal. At this dosage, compressive strength, reactivity, carbon sequestration rate, and porosity all reach their optimal values, which shows the critical importance of the Mg(NO₃)₂ level. The findings of this study expand the theoretical framework for improving LCCB's carbonation performance, offer a scientific basis for advancing the use of carbon-sequestering cement-based materials, and foster green, circular, and synergistic progress in the building materials industry.

Conventional development of low-carbon clinker mainly relies on empirical or mechanistic understanding of high-temperature and hydration reactions among clinker phases. This strategy, which combines experimental trial-and-error with mechanistic analysis, is generally time-consuming. Therefore, conventional development approaches are no longer sufficient to meet the demand for the rapid development of novel low-carbon clinkers. The development of high-throughput (HT) methods and machine learning (ML) has provided a new paradigm for the design of cement-based materials. However, available clinker data for ML-based clinker design are usually collected from scattered sources, and they differ substantially in preparation procedures, testing conditions, and compositional definitions, making them difficult to use directly. To address these issues, this study used clinker phases as compositional units and constructed a clinker dataset through a standardized HT preparation procedure. On this basis, the predictive performance of different ML models for the 28 d compressive strength of clinker was compared. Furthermore, the optimized surrogate model was coupled with a genetic algorithm to screen for low-carbon clinkers under constraints on 28 d compressive strength and CO₂ emissions. This approach enabled evaluation of the applicability of the proposed data-driven framework for the design of novel low-carbon clinkers.

In this study, 11 clinker phases were selected as the basis for compositional design, and each clinker composition prepared by dry-mixing these phases with gypsum in specified proportions was regarded as one specimen. A clinker dataset was constructed using an HT experimental procedure. The dataset contained information on phase composition, water-to-binder ratio (W/B), 3 and 28 d compressive strengths for a total of 264 specimens. Among them, 228 specimens were used for training and validation, while 36 specimens were reserved as a test set. The test set was further divided into in-system and out-of-system specimens to evaluate model stability. For model construction, five nonlinear ML algorithms were employed. The normalized phase composition after gypsum incorporation was used as the input. The 28 d compressive strength was used as the output. Model hyperparameters were determined by Bayesian optimization, and repeated constrained holdout validation was adopted to reduce the influence of random splitting on model evaluation. Model performance was evaluated using mean squared error, root mean squared error (RMSE), mean absolute error, and mean absolute percentage error. After the surrogate model had been determined, it was coupled with a genetic algorithm to search the phase composition space for low-carbon clinkers with a predicted 28 d compressive strength higher than 42.5 MPa and CO₂ emissions lower than 671 or 592 kg CO₂/t. These CO₂-emission thresholds were reduced by 15% and 25%, respectively, relative to Portland clinker.

Dataset analysis showed that the specimens in the dataset exhibited pronounced compositional dispersion and system-level heterogeneity. This heterogeneity was one of the main sources of model instability. The W/B gradually increased from the C3S/C2S-rich region toward regions enriched in aluminate/ferrite phases and C4A3$/C5S2$. The 3 d and 28 d compressive strengths of the training/validation set covered a range of 0-70 MPa, whereas the test set was mainly concentrated in the medium-to-high strength range. Some specimens exhibited strength regression from 3 d to 28 d. These complex strength-development patterns increased the difficulty of model learning but also provided useful information for identifying nonlinear relationships between composition and compressive strength. Validation results showed that, under the conditions of limited data and high compositional heterogeneity, controlled model capacity and moderate regularization were more beneficial for improving prediction stability. The optimized hyperparameters of different models all reflected a tendency to suppress overfitting. Among the models, the artificial neural network (ANN) adopted a relatively small architecture with regularization and achieved the best performance on the validation sets. The test-set results further demonstrated that the ANN had better cross-system generalization capability. Compared with the other models, the ANN achieved an overall RMSE of 3.9 MPa on the test set. Subsequently, the ANN was selected as the surrogate model and coupled with a genetic algorithm for the inverse design of low-carbon clinkers. When the CO₂-emission reduction constraint was tightened to 25%, the genetic algorithm further favored low-carbon phases and generated candidate clinkers dominated by aluminate phases or the C4A3$ phase, with CO2 emissions of approximately 555 kg CO₂/t. Experimental validation showed that the measured 28 d compressive strengths of the candidate clinker compositions were close to the model predictions. The prediction errors for all specimens were lower than 10% of the measured strengths.

This study constructs a clinker dataset based on a HT preparation procedure and establishes an ML prediction and constrained-optimization framework for screening low-carbon clinkers. The results show that the dataset is characterized by broad compositional coverage, local sparsity, and pronounced system heterogeneity. Therefore, the model needs to balance nonlinear representation capability with regularization constraints. Comparisons on the validation sets and the test set show that the low-capacity ANN with moderate regularization achieves the best performance in terms of prediction accuracy. Its RMSE is 3.9 MPa on the overall test set, and the RMSE values for the in-system and out-of-system subsets are 3.1 MPa and 4.2 MPa, respectively, indicating that it can serve as the surrogate model for inverse design of low-carbon clinker. XGBoost can be regarded as a competitive potential surrogate model, but it tends to produce relatively large prediction errors for some specimens. By coupling the ANN surrogate model with a genetic algorithm, four candidate low-carbon clinkers satisfying the 28 d compressive strength and CO₂ emission constraints are screened. Experimental validation confirms good agreement between the predicted and measured compressive strengths. These results demonstrate that the proposed dataset construction and surrogate model screening strategy can be used to explore of novel low-carbon clinkers.

The cement industry is one of the most CO₂-intensive industrial sectors, and reducing clinker consumption through mineral substitution is an effective strategy for developing low-carbon cementitious materials. Calcium carbonate (CaCO₃) is widely used as a clinker substitute because of its abundant resources, low cost, and compatibility with cement production. In addition to the thermodynamically stable calcite, CaCO₃ also exists in metastable polymorphs such as vaterite and aragonite, which differ in crystal structure, particle morphology, specific surface area, and dissolution behavior. These differences may produce distinct effects on the rheology, hydration, pore structure, and mechanical properties of cement pastes. This study aims to clarify how different CaCO₃ polymorphs regulate the coupled rheology-hydration-structure-strength relationship of cement paste.

Ordinary Portland cement pastes containing 15% (mass fraction) calcite, vaterite, or aragonite were prepared at a constant water-to-binder ratio of 0.42, with pure cement paste as the reference. Commercial limestone powder was used as the calcite source, while vaterite and aragonite were synthesized using a double-decomposition method. Rheological properties were characterized using a rotational rheometer, and the dynamic yield stress was determined by fitting the descending branch of the flow curve with the Herschel-Bulkley model. Early hydration behavior was evaluated by isothermal calorimetry. Flexural and compressive strengths were measured at 1, 3, 7 d, and 28 d. Hydration products and phase evolution were examined by X-ray diffraction and thermogravimetry, and the pore structure was characterized by mercury intrusion porosimetry.

The three CaCO₃ polymorphs exhibited distinct physicochemical characteristics and consequently exerted different effects on cement paste performance. Calcite mainly consisted of irregular blocky particles and mainly acted through dilution and filling, leading to reductions in both static and dynamic yield stress and only limited promotion of hydration and strength development. Vaterite, composed of loosely aggregated fine particles with relatively high surface activity, increased the dynamic yield stress, accelerated early hydration most effectively, promoted the formation of portlandite, chemically bound water, and carboaluminate phases, and refined the pore structure, and was therefore more beneficial for compressive strength development. Aragonite, with a needle-like morphology, reduced the dynamic yield stress but markedly increased the apparent viscosity and thixotropic index, which indicated a stronger effect on structural build-up and rebuilding. It also improved pore structure and showed the highest compressive strength at 1-7 d as well as the highest flexural strength and flexural-to-compressive strength ratio at 28 d. These results indicated that the effects of CaCO₃ polymorphs could not be attributed to a single parameter such as particle size, but were jointly governed by morphology, specific surface area, and reactivity.

Different CaCO₃ polymorphs significantly affect the rheology, hydration, and strength development of cement pastes. Calcite mainly shows weak dilution, filling, and nucleation effects, resulting in reduced yield stress and limited strength compensation. Vaterite markedly promotes early hydration and sustained product accumulation, leading to higher dynamic yield stress, increased portlandite and chemically bound water contents, greater formation of carboaluminate phases, refined pore structure, and improved early compressive strength. Aragonite, owing to its needle-like morphology, strongly enhances static yield stress, apparent viscosity, and thixotropic recovery, while also refining pore structure and achieving the highest flexural strength and flexural-to-compressive strength ratio. Compared with calcite, metastable CaCO₃ polymorphs are more effective in regulating the coupled “rheology-hydration-strength” behavior of cement pastes. Vaterite is more suitable for enhancing hydration and promoting compressive strength development, whereas aragonite is more advantageous for rheological control, flexural performance, and overall mechanical properties.

Concrete is the most widely used construction material, and its production accounts for about 8% of global CO₂emissions. Supplementary cementitious materials (SCMs), such as fly ash, are therefore essential for low-carbon concrete. However, SCMs are highly heterogeneous. Their chemical composition, especially the Al/Si ratio, varies substantially, and the cooling rate during production further affects glass structure and reactivity.

The performance of SCMs is primarily governed by the alkaline dissolution of the aluminosilicate glass phase. Although dissolution mechanisms in high-calcium glasses have been studied, low-calcium systems remain less understood. In particular, the coupled effects of composition and cooling rate on dissolution are still unclear. Topological constraint theory and molecular dynamics simulations provide a useful framework for linking atomic structure to macroscopic behavior, but their application to low-calcium systems remains limited.

This study investigates how the Al/Si ratio and cooling rate jointly affect alkaline dissolution in synthetic low-calcium aluminosilicate glass and fly ash. A mechanistic descriptor based on the glass-phase Al/Si ratio is further introduced into a prediction model to assess its engineering relevance.

Synthetic glasses with different Al/Si ratios were synthesized by melt quenching. To investigate cooling-rate effects, a glass-mullite composite (GM) was produced by slow cooling, while a pure glass (AM) with the same glass-phase Al/Si ratio was prepared for comparison. Three industrial fly ashes (FA1-FA3) were also examined. Dissolution experiments were conducted at pH 13.2 (0.14 mol/L NaOH, 20 °C) with a solid-to-liquid ratio of 1:2000. Ion concentrations were determined by inductively coupled plasma optical emission spectrometry, and dissolution rates were obtained by fitting the concentration-time curves. Ab initio molecular dynamics (AIMD) simulations were performed using the HONPAS platform to characterize the atomic-scale structure. average microcrystalline sizes were calculated from X-ray diffraction data using the Scherrer equation. Finally, a three-layer backpropagation neural network was developed using the glass-phase Al/Si ratio as the key mechanistic descriptor for compressive-strength prediction.

The dissolution rate increased linearly with the Al/Si ratio on a logarithmic scale. However, GM and fly ash dissolved more slowly than synthetic glasses with similar Al/Si ratios. Topological constraint analysis showed that this trend mainly results from the lower Al-O bond dissociation energy (501.9 kJ/mol) than Si-O (799.6 kJ/mol). AIMD results showed that increasing the Al/Si ratio reduced the coordination number of Si, while the coordination of Al remained nearly unchanged. This indicates that Al incorporation introduces tetrahedral defects in the Si-O network and weakens topological constraints. Even so, the decrease in topological bond strength was limited, suggesting that Al-O bond weakening is the dominant factor, whereas network defects play a secondary role.

Cooling rate also had a strong effect. Slower cooling increased the coordination number and the topological bond strength index, but the measured dissolution rate of AM was still lower than that predicted from topological parameters alone. Solubility analysis showed that AM and G5 had the same glass-phase Al/Si ratio, but AM had lower final solubility. According to the Ostwald-Freundlich relation, larger crystallites reduce solubility, which is consistent with slower cooling. Therefore, slow cooling suppresses dissolution through three mechanisms: mullite crystallization lowers the glass-phase Al/Si ratio, increased coordination strengthens the network, and larger crystallites reduce solubility.

Finally, incorporating the glass-phase Al/Si ratio into the BP neural network model increased the R² value from 0.484 to 0.830, confirming the key role of the microscopic Al/Si ratio in macroscopic performance evolution.

The alkaline dissolution rate of low-calcium aluminosilicate glass increases with the Al/Si ratio primarily due to the lower bond dissociation energy of Al-O (501.9 kJ/mol) compared to Si-O (799.6 kJ/mol). Tetrahedral defects and weakened topological constraints play only a secondary role. Slow cooling suppresses dissolution by reducing the glass-phase Al/Si ratio, strengthening topological constraints, and increasing average microcrystalline size. The backpropagation neural network with the glass-phase Al/Si ratio as a mechanistic input markedly improves prediction accuracy, supporting the engineering relevance of the proposed microscopic mechanism and providing a basis for the intelligent design of low-carbon concrete.

Ultra-high-performance concrete (UHPC) is widely applied in bridges, prefabricated structures, and advanced construction owing to its high strength, dense microstructure, and excellent durability performance. Its performance is strongly governed by the type and dosage of supplementary cementitious materials (SCMs), including silica fume (SF), ground granulated blast-furnace slag (GGBS), superfine GGBS (sGGBS), fly ash (FA), and fly ash microspheres (sFA), as well as polycarboxylate superplasticizer (PCE) dosage and curing age (CA). However, the relationships among mixture parameters, water distribution, hydration, and strength development are highly nonlinear, and conventional empirical models often fail to provide accurate predictions.

Water film thickness (WFT) is a key microscopic parameter reflecting the free water covering the surfaces of solid particles, directly influencing particle lubrication, dispersion, and fresh-state behaviour in high-solid-volume UHPC systems. Compressive strength (CS) is another essential index for mechanical evaluation. Accurate prediction of both WFT and CS is crucial for mixture design and performance optimization. Nevertheless, UHPC experiments are costly and time-consuming, leading to limited datasets. To overcome this small-sample challenge, this study proposes an integrated framework combining conditional tabular generative adversarial network (CTGAN) for data augmentation, Bayesian optimization (BO)-based eXtreme Gradient Boosting (XGBoost) for predictive modelling, and interpretable machine-learning methods (SHAP and PDPs) for mechanism analysis.

A total of 160 experimental data points were obtained from binary, ternary, and quaternary cementitious systems. The input variables included cement content, PCE dosage, SF, GGBS, sGGBS, FA, sFA, and CA, while the output variables were WFT and CS. P·I 42.5 cement was used as the reference cement, and quartz sand with particle sizes of 0.84-2.00 mm was used as the aggregate. In the binary systems, one mineral admixture was used to replace cement by volume at replacement levels of 0%, 5%, 10%, 15%, and 20%. In the ternary systems, the SF content was fixed at 10%. In the quaternary systems, the GGBS content was fixed at 20% based on the ternary system. WFT was calculated using the wet packing method, and CS was measured after curing for 3, 7, 28 d, and 90 d.

CTGAN was used for data augmentation to expand the original small dataset. The model learned the joint distribution of the real experimental data through adversarial training between the generator and discriminator. After optimization, 1000 synthetic samples were generated for WFT prediction, and 900 synthetic samples were generated for CS prediction. The quality of the synthetic data was evaluated by comparing histograms, cumulative distribution curves, and correlation matrices between real and synthetic datasets.

The synthetic datasets were used to train XGBoost regression models. BO was applied to optimize hyperparameters, including tree depth, learning rate, number of estimators, subsample ratio, column-sampling ratio, minimum child weight, and regularization. Model performance was evaluated by R², MAPE, RMSE, and MAE. SHAP and PDPs were employed to interpret feature contributions and nonlinear effects.

The comparison between real and synthetic datasets showed that CTGAN effectively preserved the main distributional characteristics of the original data. The generated samples had value ranges and distribution patterns similar to those of the real samples. The cumulative distribution curves also showed good consistency between real and synthetic datasets. In addition, the correlation matrices indicated that CTGAN captured not only the marginal distribution of individual variables but also the correlation structure among key variables. Therefore, the generated data could provide a reliable data basis for BO-XGBoost model training under small-sample conditions.

The BO-XGBoost model exhibited excellent prediction performance. For WFT, test R² reached 0.986, with training and test RMSE values of 0.909 3 nm and 0.387 6 nm, respectively. For CS, test R² was 0.976, with RMSE values of 5.244 8 MPa and 3.837 9 MPa. Predicted values clustered around the 45° ideal line, with most samples within ±10% error. Residuals were concentrated and symmetric, indicating no systematic bias, and loss curves showed stable convergence without overfitting. These results demonstrate that CTGAN augmentation effectively improved the ability of XGBoost to capture complex nonlinear relationships under small-sample conditions.

The SHAP and PDP analyses further revealed the influence mechanisms of different variables. For WFT, sFA showed a strong positive contribution, mainly because its fine and spherical particles improved particle dispersion and increased effective water-film formation. SF had a positive effect at low-to-medium dosages, but excessive SF could reduce WFT because of its high specific surface area. GGBS and sGGBS tended to show negative effects at high dosages, which may be related to denser particle packing and compression of effective water-film space. PCE showed a nonmonotonic effect: moderate PCE improved WFT, while excessive PCE could weaken the stability of the water-film structure.

For CS, CA was the most important factor and showed a strong positive contribution, reflecting continuous hydration and microstructure densification with curing time. sGGBS, SF, and sFA generally promoted strength development. sGGBS contributed to strength through microfilling and potential pozzolanic activity, while SF improved strength through pore refinement and matrix densification. In contrast, GGBS and FA showed negative effects at high dosages because excessive cement replacement reduced clinker content and limited the availability of calcium hydroxide (Ca(OH)₂) for secondary reactions. These results demonstrate that different mineral admixtures have dosage-dependent and material-specific effects on UHPC performance.

This study establishes a multi-response prediction and interpretation framework integrating CTGAN, BO-XGBoost, SHAP, and PDPs for UHPC under small-sample conditions. CTGAN effectively generated high-quality synthetic data while preserving original distribution and correlation characteristics. The optimized BO-XGBoost model achieved outstanding predictive accuracy and generalization, with test R² of 0.986 for WFT and 0.976 for CS. The interpretability analysis demonstrated that the effects of mineral admixtures on WFT and CS are highly dependent on material type and dosage: sFA and SF are beneficial for WFT within suitable ranges, whereas sGGBS, SF, and sFA promote CS; excessive GGBS and FA impair CS due to limited reaction activity. The proposed framework offers an effective data-driven approach for UHPC mixture design, performance prediction, and mechanism interpretation when only limited experimental data are available.

Externally applied electric fields are widely used in rapid chloride migration coefficient tests and electrochemical repair or protection of concrete structures. These techniques accelerate the directional migration of ions in the pore solution and are important for evaluating chloride resistance and controlling reinforcement corrosion risks. However, concrete is a heterogeneous porous material composed of mortar, interfacial transition zones, and aggregates. Under strong electric fields, the distributions of electric-potential, ion concentration and migration flux become strongly coupled. The conventional Poisson-Nernst-Planck model usually assumes a constant ionic mobility, implying that the electromigration flux increases approximately linearly with the electric field intensity. This assumption may be insufficient under strong electric-field conditions because local electric-field concentration, concentration polarization and ion crowding can reduce the effective ionic mobility. As a result, the chloride migration front and migration flux may show nonlinear evolution. To address this issue, this work develops a physics-informed neural network framework for identifying a field-dependent ionic-mobility relation and analyzing multi-ion electromigration in concrete under strong electric fields.

A multi-ion electromigration model was established based on the Poisson-Nernst-Planck equations and the Langmuir chloride-binding relation. Four representative ions in RCM tests, i.e., Na⁺, K⁺, Cl^- and OH^-, were considered. A field-dependent mobility-suppression function was introduced into the electromigration term to describe the nonlinear attenuation of effective ionic mobility under strong electric fields. The concrete specimen was represented as a two-dimensional, three-phase mesoscopic domain consisting of mortar, interfacial transition zones, and aggregates. Aggregates were regarded as impermeable, while ion transport occurred in mortar and interfacial transition zones.

To identify the mobility suppression parameters from limited chloride profiles, an importance-adaptive-training physics-informed neural network was adopted. The governing equations, boundary conditions, initial conditions and experimental chloride-concentration profiles were embedded into the loss function. The neural network took dimensionless coordinates, time and voltage as inputs, and output the normalized electric potential and ion concentrations. The shape parameters of the mobility suppression function were optimized together with the neural network parameters. RCM data reported by Spiesz and Brouwers were used for model validation. The chloride profile at 47.5 V was used for parameter identification, while the profiles at 35 V and 60 V were used to evaluate the cross-voltage prediction capability. Prediction accuracy was evaluated using RMSE and R².

The identified mobility suppression parameters obtained by IAT-PINN were (α, γ) = (0.74, 1.37), while the least-squares (LSQ) method gave (α, γ) = (0.03, 1.04). The LSQ result was closer to the boundary of the search domain, indicating possible parameter nonuniqueness when only discrete chloride profiles were fitted. In contrast, the IAT-PINN introduced governing equations and boundary constraints into the inversion process, which improved the stability of parameter identification. When the proportion of training samples decreased, the IAT-PINN maintained lower RMSE and higher R² than LSQ, showing better data efficiency under sparse observations.

The same mobility parameters identified at 47.5 V were then used to predict chloride profiles at 35 V and 60 V without recalibration. The model captured the voltage-dependent advancement of the chloride migration front and agreed well with the measured profiles. The improvement was more evident at high voltage, indicating that the field-dependent mobility relationship reduced the prediction bias caused by the constant-mobility assumption.

The identified suppression function further revealed a transition from a driving force-controlled regime to a mobility-limited regime. At low electric field intensity, the suppression factor was close to 1 and the migration response was nearly linear. As the electric field increased, the effective mobility decreased, causing the chloride migration contribution to reach a peak and then decline. Multi-ion concentration profiles showed localized migration bands for Cl^-, K⁺, Na⁺ and OH^-, whose fronts gradually slowed with time. The data-assimilation correction was mainly concentrated near the chloride migration front, confirming that experimental data selectively constrained the high-gradient transport region rather than shifting the entire concentration field.

A field-dependent mobility-suppression relation was incorporated into a multi-ion Poisson-Nernst-Planck model and identified using an importance-adaptive physics-informed neural network. The proposed framework enabled the joint reconstruction of ionic concentration fields, electric-potential fields and mobility parameters from limited RCM data. Compared with least-squares (LSQ) fitting, the IAT-PINN improved parameter stability under sparse data by introducing physical constraints. The identified mobility relation showed that chloride migration under strong electric fields is not linearly enhanced with increasing voltage. Instead, effective mobility attenuation can lead to saturation or reduction of the migration contribution. Multi-ion concentration profiles and data assimilation corrections further confirmed the localized and migration-limited characteristics of strong electric-field electromigration. The proposed method provides a theoretical basis for interpreting RCM test results and identifying field-dependent mobility parameters in concrete.

Hydraulic normal concrete is required to satisfy multiple performance demands during mixture proportioning, construction, and long-term service. Its fresh-state workability affects transportation, placement, and vibration; early-age compressive strength governs construction sequence and early load-bearing capacity; standard-age compressive strength is directly related to strength grade assessment; and drying shrinkage reflects dimensional stability of hardened concrete. In engineering practice, these properties are not controlled only by deterministic target values, but are usually associated with allowable fluctuations, quality assurance requirements, and strength guarantee rates. Owing to the multiphase and heterogeneous nature of concrete, the measured properties may still fluctuate even under similar mixture proportions, raw material conditions, and curing conditions. Therefore, deterministic point prediction is insufficient for describing the reliability of performance predictions.

Machine learning has provided an effective data-driven approach for modeling the nonlinear relationship between mixture composition and concrete properties. Previous studies have demonstrated its applicability in predicting compressive strength, slump, shrinkage, durability-related indicators, and other engineering properties of cement-based materials. Multi-output and multitask learning methods further enable simultaneous prediction of multiple performance indicators by exploiting shared information among related objectives. However, most existing models for concrete performance prediction still focus mainly on deterministic outputs, while uncertainty quantification, prediction interval construction, and credibility evaluation remain insufficiently explored, especially for hydraulic normal concrete. To address this issue, this study developed a hierarchical multi-task heteroscedastic probabilistic neural network (HMT-HPNN) for probabilistic multitarget performance prediction and credibility evaluation of hydraulic normal concrete.

A multitarget performance database of hydraulic normal concrete was established. The database contained 457 samples, with 45 raw input variables related to cement, fly ash, aggregates, and mixture proportions. Four output targets were selected, including slump, 7 d compressive strength, 28 d compressive strength, and 28 d drying shrinkage. The development database was divided into a training set and a test set at an 8:2 ratio, corresponding to 365 and 92 samples, respectively. In addition, 46 homologous engineering samples were used as an engineering validation set to evaluate the generalization ability of the proposed model in an engineering scenario.

Four neural network models were constructed and compared, including Single-target MLP, Multi-output MLP, MLP + MC Dropout, and HMT-HPNN. The Single-target MLP was used as an independent deterministic modeling baseline, while the Multi-output MLP was adopted as a conventional deterministic multi-output baseline. The MLP + MC Dropout was selected as a classical probabilistic neural-network baseline, in which multiple stochastic forward passes were used to estimate predictive uncertainty. The proposed HMT-HPNN consisted of a shared representation module, a hierarchical task representation module, task-specific branches, and heteroscedastic probabilistic subnetworks. For each target, the model simultaneously output the predictive mean and log-variance, and was trained using a heteroscedastic Gaussian negative log-likelihood (NLL) loss.

To ensure a fair comparison, all models were optimized using the same data partition and preprocessing strategy. The training set was further divided using fixed five-fold stratified cross-validation based on the quantiles of 28 d compressive strength. Randomized grid search was adopted for hyperparameter optimization. Deterministic models were mainly selected according to the five-fold average NRMSE, while probabilistic models were selected by jointly considering prediction accuracy and negative log-likelihood. The final models were retrained on the complete training set and evaluated on the test set. The evaluation metrics included R², MAE, NRMSE, PICP90, NLL, calibration curves, 90% prediction intervals, composite uncertainty, composite error, and credibility classification.

The target distribution and correlation analysis showed that the four properties had different statistical characteristics and material-control mechanisms. The 7 d and 28 d compressive strengths exhibited a strong positive correlation, reflecting the continuity of strength development. In contrast, slump and 28 d drying shrinkage showed weak linear correlations with strength targets, indicating that workability and dimensional stability should be treated as independent prediction targets rather than being inferred from compressive strength alone. For prediction accuracy, the Single-target MLP achieved the lowest average test NRMSE among the four models. However, the overall accuracy differences among Single-target MLP, Multi-output MLP, MLP + MC Dropout, and HMT-HPNN were relatively small. HMT-HPNN retained reliable regression capability after introducing probabilistic outputs. On the test set, its R² values for 7 d compressive strength, 28 d compressive strength, and 28 d drying shrinkage were approximately 0.875, 0.868, and 0.872, respectively. Slump showed relatively lower prediction accuracy, which was associated with its higher sensitivity to aggregate moisture content, water demand, admixture dispersion, and construction-related disturbances.

For uncertainty quantification, HMT-HPNN outperformed MLP+MC Dropout. The PICP90 values of HMT-HPNN for slump, 7 d compressive strength, 28 d compressive strength, and 28 d drying shrinkage were 86.96%, 90.22%, 91.30%, and 93.48%, respectively, which were closer to the nominal confidence level of 90%. In addition, HMT-HPNN achieved lower NLL values for all four targets, indicating a more appropriate balance between prediction error and predicted variance. The calibration curves also showed that HMT-HPNN generally provided better agreement between nominal confidence levels and empirical coverage rates. The 90% prediction intervals generated by HMT-HPNN achieved an overall coverage rate of 90.49% on the test set, demonstrating that the heteroscedastic uncertainty output could be effectively transformed into reliable prediction intervals. To evaluate the risk-indication ability of model uncertainty, composite uncertainty was calculated from the normalized predictive standard deviations of the four targets, while composite error was calculated from the normalized absolute prediction errors. The testing results showed that composite error increased with the composite uncertainty level, confirming that the uncertainty output of HMT-HPNN was informative in identifying sample-level prediction risk.

For the homologous engineering validation set, HMT-HPNN maintained satisfactory generalization performance. The R² values for slump, 7 d compressive strength, 28 d compressive strength, and 28 d drying shrinkage were 0.70, 0.94, 0.90, and 0.87, respectively. The PICP90 values for all four targets reached or exceeded 90%, and an effective correspondence was observed between the level of uncertainty and the prediction error. These results indicate that HMT-HPNN maintained its accuracy, uncertainty quantification capability, and credibility evaluation ability under homologous engineering conditions.

A probabilistic multitarget prediction framework was developed for hydraulic normal concrete. The proposed HMT-HPNN simultaneously predicted slump, 7 d compressive strength, 28 d compressive strength, and 28 d drying shrinkage by integrating shared representation, hierarchical task representation, task-specific branches, and heteroscedastic probabilistic subnetworks. Although the point prediction accuracy of HMT-HPNN was not the highest among all compared models, its testing accuracy was comparable to that of conventional neural network models, confirming its reliable regression capability. Compared with MLP + MC Dropout, HMT-HPNN provided superior uncertainty quantification, with PICP90 values closer to the nominal 90% confidence level and lower NLL values for all four targets. The overall coverage rate of the 90% prediction intervals reached 90.49%, indicating that the model uncertainty could be effectively converted into reliable prediction intervals. The composite uncertainty was consistent with the composite prediction error, which confirmed the ability of HMT-HPNN to identify sample-level prediction risks. Independent homologous engineering validation further demonstrated the generalization capability of HMT-HPNN. The model maintained satisfactory accuracy, reliable prediction interval coverage, and effective credibility classification on 46 engineering validation samples. Therefore, the proposed framework provides a data-driven method for multitarget performance prediction, prediction interval construction, and credibility evaluation of hydraulic normal concrete, and can support mixture proportion design and performance risk assessment.

Ultra-high-performance concrete (UHPC) has been widely used in bridges, tunnels, prefabricated structures, and other demanding engineering applications owing to its high compressive strength, excellent toughness, and superior durability. However, conventional UHPC typically requires a high cement content, high dosages of reactive powders, and steel fiber reinforcement, leading to considerably higher carbon emissions than ordinary concrete. The partial replacement of ordinary Portland cement (OPC) with low-carbon cementitious materials, such as ground granulated blast-furnace slag, fly ash, silica fume, limestone powder, belite cement, and other supplementary cementitious materials, has therefore become an important strategy for developing low-carbon UHPC. The incorporation of low-carbon binders enhances sustainability but also increases the complexity of mixture proportion design. Variations in oxide composition, reaction activity, particle size distribution, and hydration products jointly affect the compressive strength of low-carbon UHPC through hydration reactions, particle packing, fiber reinforcement, and curing conditions. Although machine learning models have shown good prediction performance in concrete strength prediction, most of them remain black-box approaches and provide limited physical interpretability. Post-hoc explanation methods, such as feature importance analysis, SHAP, and LIME, can identify statistical associations, but they may generate physically unrealistic virtual mixture proportions when strict mix proportion constraints and material boundaries are ignored. To address these limitations, this study proposes an interpretable Bayesian network (BN) modelling framework for the intelligent design of low-carbon UHPC. The proposed framework integrates hydration-packing mechanism priors with data-driven structure learning, aiming to describe the conditional dependencies among mixture proportion parameters, intermediate mechanism variables, and 28 d compressive strength. By introducing physically meaningful intermediate variables, the model is expected to provide both reliable strength prediction and mechanistic interpretation for low-carbon UHPC mixture design.

A total of 491 original UHPC mixture proportion records were collected through a systematic literature review following the PRISMA guidelines. After unit standardization, variable transformation, outlier checking, and valid sample screening, 136 valid samples were retained for model construction. The target variable was the 28 d compressive strength of UHPC, denoted as Z₁. To improve the physical interpretability of the model, the original high-dimensional mixture proportion variables were transformed into a hydration-packing feature system. The material/process variables included the cementitious material reaction activity index X₀, water-to-binder ratio X₁, activator-to-binder ratio X₂, binder-to-sand ratio X₃, and steel fiber volume fraction X₄. The intermediate mechanism variables included the modified CaO/SiO₂ molar ratio Y₁, the modified SO₃/Al₂O₃ molar ratio Y₂, and the initial particle packing density Y₃. These variables were used to represent C-S-H formation potential, sulfate-aluminate reaction balance, and the initial physical compactness of the particle system, respectively. Based on the mechanism of low-carbon UHPC strength formation, a three-layer BN structure was constructed, consisting of a material and process parameter layer, an intermediate mechanism layer, and a target performance layer. Mechanism-informed constraints were introduced to prevent physically unreasonable paths, such as reverse links from compressive strength to mix design variables. Structure learning was performed using a combined strategy involving the hill-climbing, MMHC, and Tabu Search algorithms. bootstrap resampling was adopted to evaluate edge stability, and the BGe score was used as the main scoring criterion, with BIC and AIC used for sensitivity comparison. For parameter learning, maximum likelihood estimation was used for auxiliary variables, while Bayesian ridge regression was adopted for continuous compressive strength prediction. To reduce the risk of data leakage during model evaluation, all preprocessing steps, including variable transformation, standardization parameter estimation, outlier threshold determination, BN structure learning, bootstrap-based stable edge selection, and parameter learning, were embedded within the training folds of a 10-fold cross-validation procedure. The validation folds were used only for final performance evaluation.

The proposed BN model achieved good prediction accuracy for the 28 d compressive strength of low-carbon UHPC. Based on 10-fold cross-validation and Bootstrap evaluation, the model obtained an average RMSE of 0.281 3, an average MAE of 0.203 1, and an average R² of 0.910 1. The predicted mean compressive strength was 162.11 MPa, with a standard deviation of 24.53 MPa and an average confidence interval width of 46.04 MPa. Compared with random forest and support vector regression models, the BN model provided comparable prediction performance while also offering explicit conditional dependency paths and uncertainty representation. Correlation analysis indicated that curing temperature, pozzolanic cementitious materials, quartz sand, and steel fiber content generally were positively correlated with compressive strength, whereas the water-to-binder ratio and cement content were weakly negatively correlated. However, Pearson correlation analysis describes only linear statistical relationships and could not reveal hierarchical conditional dependencies among variables. Therefore, BN modeling was necessary to identify indirect effects and mechanism-related pathways. The final BN structure demonstrated that the modified CaO/SiO₂ molar ratio Y₁ acted as an important upstream node associated with compressive strength. This indicates that the active oxide composition of cementitious materials plays a fundamental role in low-carbon UHPC strength formation. The modified SO₃/Al₂O₃ molar ratio Y₂ served as an important intermediate node, reflecting the role of sulfate-aluminate reaction balance in hydration product formation. The initial particle packing density Y₃ was directly related to the physical compactness of the matrix and contributed substantially to strength development. Mechanism-informed intervention sensitivity analysis further showed that Y₁, Y₂, Y₃, and their interaction contributed 28.7%, 19.2%, 35.1%, and 17.0% to compressive strength, respectively. When Y₁ and Y₂ were combined as the hydration effect, the hydration effect, packing effect, and hydration-packing interaction contributed 47.9%, 35.1%, and 17.0%, respectively. These results suggest that the strength development of low-carbon UHPC is jointly controlled by hydration reactions, particle packing, and their nonlinear coupling. Therefore, intelligent mix design should prioritize the optimization of active oxide ratios, initial particle packing density, and their compatibility with the binder-to-sand ratio, water-to-binder ratio, and steel fiber content. A preliminary independent validation using five unseen low-carbon UHPC mixtures showed that the measured 28 d compressive strengths were consistent with the BN-predicted values and fell within the 95% prediction intervals. The standard deviation of prediction bias was 6.46 MPa, and the average relative deviation was approximately 0.45%. This provides preliminary support for the applicability of the proposed framework for candidate mix screening and strength prediction. Nevertheless, because the number of validation mixtures was limited, further experimental data covering broader cementitious systems, curing regimes, and fiber contents are still needed to improve the generalizability of the model.

In this study, an interpretable Bayesian network modelling framework was developed for the intelligent design of low-carbon UHPC. By integrating hydration-packing mechanism priors with data-driven structure learning, the proposed model described a hierarchical probabilistic relationship among material and process parameters, intermediate mechanism variables, and 28 d compressive strength. The mechanism-constrained BN model achieved high prediction accuracy under small-sample and high-dimensional conditions, with an average R² of 0.91 based on 10-fold cross-validation. Compared with conventional black-box machine learning approaches, the proposed BN framework not only maintained competitive predictive performance but also provided interpretable conditional dependency paths and uncertainty estimation. The modified CaO/SiO₂ molar ratio, modified SO₃/Al₂O₃ molar ratio, and initial particle packing density were identified as key mechanism variables governing compressive strength. The intervention sensitivity analysis showed that hydration effects (47.9%), packing effects (35.1%), and hydration-packing interactions (17.0%) jointly controlled the strength formation of low-carbon UHPC. Independent experimental validation provided preliminary support for the applicability of the proposed framework for candidate mix screening and strength prediction. This method provides a useful probabilistic modeling tool for the interpretable and intelligent design of low-carbon UHPC.

Machine learning has been widely applied to various concrete materials tasks, such as macroscopic performance prediction and mix proportion optimization. The performance of machine learning models depends strongly on the quantity and quality of the available datasets. However, constructing large-scale and high-quality concrete mix proportion datasets remains challenging because concrete experiments are time-consuming, costly, and easily affected by material sources, curing conditions, and human operations. At present, small-sample modeling problems are mainly addressed through two strategies: model optimization and data augmentation. Model optimization methods mainly improve the extraction of information from existing small-sample datasets by adjusting model structures or training strategies. However, they do not fundamentally increase the amount of data, and their improvement in prediction accuracy is still limited by the original sample size. Conventional data augmentation methods mainly rely on generative adversarial networks, which learn the real data distribution through adversarial training between a generator and a discriminator, thereby generating virtual samples to expand the original dataset. Nevertheless, when dealing with complex tabular data, generative adversarial networks may encounter unstable training, insufficient sample diversity, and distribution shifts in generated data, resulting in limited consistency between virtual and real data. To address these issues, this study proposes MixDiff, a denoising diffusion probabilistic model (DDPM) for generating virtual concrete mix proportion data under small-sample conditions. This model aims to generate high-quality virtual concrete mix proportion data.

A small-sample dataset containing 100 sets of concrete mix proportions was first established. The input features included water, cement, fly ash, mineral fines, river sand, manufactured sand, coarse aggregate, water-reducing agent, total binder content, water-to-binder ratio, total fine aggregate content, and sand ratio. The target variables included 7 d compressive strength, 28 d compressive strength, slump, expansion degree, initial setting time, and final setting time. MixDiff was developed using a fully connected neural network and a denoising diffusion probabilistic model. The model consisted of a sinusoidal timestep embedding module and four stacked residual fully connected blocks. Each residual block contained fully connected layers, batch normalization layers, and SiLU activation functions. In the forward diffusion process, Gaussian noise was progressively added to the normalized data according to a cosine variance schedule. In the reverse generation process, a neural network was used to predict the noise and progressively reconstruct concrete mix proportion samples with realistic distribution characteristics from standard Gaussian noise. Subsequently, the quality of virtual data generated by MixDiff was quantitatively evaluated from two perspectives: marginal distribution and feature correlation. Under five-fold cross-validation and grid search, several mainstream machine learning models were further used to compare the prediction performance of the baseline mode and the augmentation mode. Finally, 15 mix proportions were selected from the generated virtual dataset for experimental validation to evaluate the engineering plausibility of the generated data.

The quantitative evaluation results showed that the virtual dataset generated by MixDiff showed high similarity to the real dataset. The average Wasserstein distance, K-S statistic, and J-S divergence between the two datasets were 0.019 5, 0.095 2, and 0.120 7, respectively. The Frobenius distance between their Pearson correlation matrices was 0.332 7, indicating that MixDiff effectively preserved the marginal distribution characteristics of the real data and the correlation structure among variables. The machine learning comparison results showed that the prediction performance of all models improved significantly after the introduction of MixDiff-generated data. In the baseline mode, the average R² of the seven models was 0.817 1, and increased to 0.953 7 in the data augmentation mode. Meanwhile, the average RMSE decreased from 14.20 to 7.39, and the average MAE decreased from 8.77 to 4.66. Correspondingly, R² increased by 16.72%, while RMSE and MAE decreased by 47.96% and 46.86%, respectively. Among the machine learning models, the deep neural network achieved the best performance after data augmentation, with R², RMSE, and MAE values of 0.959 4, 7.00, and 4.52, respectively. In addition, the standard deviations of the five-fold cross-validation results were significantly reduced in the data augmentation mode. This indicates that MixDiff-generated data not only improved the prediction accuracy of machine learning models but also enhanced model stability and generalization ability. Experimental validation of 15 virtual mix proportions showed that the data generated by MixDiff had high plausibility in six key performance indicators, including 7 d compressive strength, 28 d compressive strength, slump, expansion degree, initial setting time, and final setting time. The average MAPE of each performance indicator was lower than 5%, and the overall average MAPE was 3.8%.

This study proposed MixDiff, a denoising diffusion probabilistic model for concrete mix proportion data generation. The results show that the virtual data generated by MixDiff were highly consistent with the real data in terms of marginal distribution and feature correlation. The Frobenius distance between the Pearson correlation matrices was only 0.332 7. After introducing MixDiff-generated data, the prediction performance of multiple machine learning models, including RF, SVR, KNN, XGBoost, LightGBM, CatBoost, and DNN, was significantly improved, with R² values of all models increasing above 0.94. Based on the average performance of the seven machine learning models for the six output performance indicators, R² increased by 16.72%, while RMSE and MAE decreased by 47.96% and 46.86%, respectively. Among these models, DNN achieved the highest R² and the lowest RMSE in the data augmentation mode. Experimental validation of 15 virtual mix proportions showed that the generated results were highly consistent with the measured results in terms of 7 d compressive strength, 28 d compressive strength, slump, expansion degree, initial setting time, and final setting time. The MAPE of each performance indicator was below 5%, and the overall average MAPE across the six performance indicators was 3.8%. These results indicate that MixDiff can learn the underlying relationships between concrete mix proportions and macroscopic properties, and it can serve as an effective method for concrete mix proportion data augmentation under small-sample conditions.

The chemical composition of cement clinker, particularly the percentage of key oxides, is crucial for evaluating cement quality and is vital for engineering-accident liability tracing. However, traditional methods for chemical composition analysis rely heavily on unhydrated original cement samples, which are often missing, improperly stored, or ambiguously labeled in engineering management, resulting in an insufficient basis for subsequent identification. Once cement hydrates, active minerals undergo irreversible reactions, making it challenging to invert the original clinker composition directly from the hardened paste. In contrast, backscattered electron (BSE) images of the hardened cement paste contain sufficient physical information to distinguish the original chemical compositions of different cement clinkers, thus providing a potential pathway for post-event tracing. Although deep learning methods have been explored for microstructural image mapping to infer macroscopic properties, conventional regression models are typically "black-box" point predictors that cannot quantify prediction uncertainty. The conditional probability density estimation method can not only provide the most probable predicted value, but also output the complete estimated probability-density curve along with its confidence assessment, thereby enabling quantitative characterization of prediction uncertainty. Accordingly, this study proposes a conditional probability density estimation method based on a Deconvolutional Density Network (DDN). Utilizing BSE images of hardened paste together with encoded water-to-cement (W/C) ratios and curing ages as conditional inputs, this model directly predicts the full conditional probability density of the main clinker oxide contents, providing a new objective and quantitative pathway for quality liability tracing.

Cement clinker samples from 47 different plants were used to prepare cement paste specimens with W/C ratios ranging from 0.25 to 0.70 and curing ages from 1 d to 28 d. The hydration was terminated by immersion in anhydrous ethanol, and the samples were polished for subsequent scanning. BSE images were acquired via a JSM-7610F field-emission SEM at 200× magnification and an accelerating voltage of 15 kV. For each sample, two scans were performed, and nine non-overlapping regions were captured per scan, resulting in a dataset of 844 independent BSE images. The proposed model consists of a feature extraction layer based on a residual network (ResNet) with self-supervised learning and cross-layer skip connections, a conditional encoder, and deconvolutional layers. The ResNet extracts multiscale morphological features from local textures to global patterns. The conditional encoder embeds the W/C ratio and curing-age conditions into the image features. The DDN is then employed to output the conditional probability density distributions for the percentages of eight oxides (CaO, SiO₂, Al₂O₃, Fe₂O₃, SO₃, Na₂O, MgO, and K₂O). The deconvolutional layers map the fused features to a smooth probability distribution over 256 discretized intervals. To evaluate the model, accuracy (ACC) under different confidence levels (α), mean absolute error (MAE), and mean squared error (MSE) were adopted as evaluation metrics.

The estimation results of the proposed model indicate that, at a confidence level of α = 90%, the model achieves an accuracy of over 90% in estimating the percentage contents of five oxides (SiO₂, Al₂O₃, Fe₂O₃, SO₃, and Na₂O) on the test set. When the confidence level is relaxed to α = 75%, the estimation accuracy for all eight oxides exceeds 90%. This could be attributed to the DDN's capability of modeling complete probability density distributions instead of simple point estimates, effectively resolving the nonunique-solution problem stemming from the intrinsic information ambiguity of BSE imaging. In terms of MAE and MSE metrics, the model exhibits relatively higher absolute errors for components with higher contents, such as CaO. This is generally attributed to the nature of absolute error metrics, which are positively correlated with the oxide content. Conversely, low-content oxides like Na₂O show smaller estimation errors, possibly because their larger fluctuation ranges among different samples allow the model to capture clearer mapping relationships. In addition, ablation experiments further reveal that removing the W/C ratio and age inputs leads to a decrease in estimation accuracy across all confidence levels, demonstrating the effectiveness of the conditional encoder in integrating critical hydration parameters. Compared with the deterministic ResNet regression model, the proposed DDN model exhibits significantly better stability of estimation performance under higher precision requirements. This result demonstrates that the DDN model effectively mitigates the inherent uncertainty in the mapping from BSE images to chemical compositions, a limitation typical of deterministic regression models that lack reliability quantification.

The main conclusions of this study are summarized as follows. The proposed probability density estimation method successfully maps BSE images of hardened cement paste to the oxide contents of clinker. Experimental results demonstrate that the model achieves an accuracy of over 90% for five oxides at a confidence level of 90%. The adoption of the Deconvolutional Density Network enables the model not only to output the most probable value of each oxide content but also to provide a complete probability density curve with corresponding confidence assessment, thereby overcoming the limitation of traditional regression models that lack quantifiable reliability for quality tracing applications. Moreover, the ResNet-based feature extraction with cross-layer skip connections effectively captures multiscale features from BSE images, while the integration of W/C ratio and curing age via the conditional encoder significantly enhances the model predictive capability. This study demonstrates that the probabilistic retrospective estimation based on BSE images and DDN offers a robust and objective means to quantitatively infer the original clinker composition from hardened paste. As such, it holds promise as a transformative technique for cement-quality arbitration in engineering-accident liability investigations.

Thermodynamic modeling has become a useful tool for understanding hydration, phase evolution, modification mechanisms, and the durability of magnesia cements. It can predict stable phases, pore solution chemistry, saturation indices, and phase stability boundaries under defined conditions, providing theoretical support for mix proportion design and performance optimization. Existing thermodynamic modeling studies have investigated the effects of raw-material mix proportions, temperature, modifiers, carbonation, and aggressive ions on the phase assemblages of M-S-H, MPC, MOS, MOC, and RMC systems, and considerable progress has been made in understanding the formation and transformation of M-S-H/M-(A)-S-H gels, magnesium phosphate hydrates, basic magnesium sulfate and chloride hydrate phases, and magnesium carbonate hydrates. However, several limitations remain. Thermodynamic data for M-S-H/M-(A)-S-H gels, magnesium carbonate hydrates, metastable intermediate products, and the temperature-dependent parameters of some hydration products are still incomplete. In addition, high-ionic-strength pore solutions require appropriate activity coefficient models, but Pitzer parameters for multicomponent systems remain insufficient. Moreover, thermodynamic modeling mainly reflects equilibrium or quasi-equilibrium states and cannot directly describe MgO dissolution, nucleation, growth, transport, or early hydration kinetics. Future work should focus on improving thermodynamic databases, refining activity models for high-salinity systems, and coupling thermodynamic modeling with experimental characterization, kinetic modeling, and transport simulations. This will enhance its reliability in guiding material design, durability assessment, and low-carbon applications.

Fluorophosphate (FP) laser glass offers high rareearth solubility, low nonlinear refractive index, and a large Abbe number, making it a promising gain medium for high-power fiber lasers. The determination of the glass-forming region is fundamental to developing new laser glass systems. Previous studies have relied heavily on trialanderror approaches, yielding only limited data at prohibitive cost in terms of manpower, finances, and material resources. In this work, thermodynamic calculations are employed to investigate the glass-forming region of multicomponent FP glass systems. The results reveal that thermodynamic calculations can effectively predict the glass-forming region of FP glasses. Moreover, differences in cation field strength and interactions with anions are identified as critical factors governing the glassforming ability and structural stability of FP glasses. Based on these theoretical predictions, a ytterbiumdoped, high-gain FP laser glass and fiber are designed and fabricated through compositional optimization, and initial 1.0 μm laser output is successfully demonstrated.

The thermodynamic calculations are based on the free energy balance between liquid and solid phases in binary systems. By solving the equilibrium equation, the melting temperature is obtained as a function of composition, and the eutectic point is determined by locating the minimum of this function. For ternary and higher-component systems, the binary eutectic points are treated as “quasi-compounds”, and the calculation is performed iteratively to construct the theoretical glass-forming region. The predicted regions were validated against experimental data reported in the literature. Raman spectroscopy combined with peak-fitting analysis was used to quantify Qn species, while density and refractive index measurements were carried out for physical property evaluation. Yb³⁺-doped FP glasses and fibers were then fabricated based on the predicted glass-forming region, and their laser performance was characterized.

Quantitative comparison between predicted and experimental glass-forming regions shows overlap ratios of 74.5%, 100%, and approximately 100% for the three systems examined, confirming that the method effectively locates the main compositional range of glass formation. Raman analysis reveals a transition from Q²-dominated chain structures at high phosphate contents to Q⁰/Q¹ units at low phosphate contents, with the Q² fraction decreasing from 73.4% to 8.4% as phosphate content drops from 80%(mole fraction) to 40%. Cation field strength is identified as a critical factor: the higher field strength of Zn²⁺ (0.43) relative to Ba²⁺ (0.24) leads to stronger network bonds but greater sensitivity to composition variation, resulting in a narrower glass-forming region. Guided by the theoretical predictions, Yb³⁺-doped FP fibers were fabricated with a peak gain coefficient of 5.56 dB/cm at 1064 nm and stable dual-wavelength laser emission at 1023.3 nm and 1031.5 nm under 974 nm pumping.

This work demonstrates that thermodynamic calculations based on eutectic point prediction provide a valid pre-screening tool for locating glass-forming regions in multicomponent FP systems. Cation field strength governs the glass-forming window and structural stability. Guided by theoretical predictions, Yb³⁺-doped FP fibers were successfully fabricated with stable 1.0 μm laser output, validating the effectiveness of this combined theoretical-experimental approach for developing novel laser glass materials.

Alkali-free aluminosilicate glasses, characterized by high hardness, high strain point, high electrical insulation, excellent chemical durability, and tunable thermal expansion coefficients, are preferred substrate materials for high-end OLED displays. In this study, glass samples with a fixed total alkaline-earth oxide content of 26.17% (molar fraction) and varying Mg/Ca/Sr ratios were prepared by the melt-quenching method. The coordination environments of ²⁹Si and oxygen atoms were characterized by magic-angle spinning nuclear magnetic resonance (MAS-NMR) and X-ray photoelectron spectroscopy (XPS), and first-principles calculations were performed to resolve atomic-scale network-structure evolution and elucidate the structural origin of the ternary MgO-CaO-SrO mixed alkaline-earth effect (MAEE). The results show that the short-range structures of network formers (Si-O, Al-O, B-O) exhibit high stability during alkaline-earth substitution. Electron localization function (ELF) analysis quantitatively shows that high-field-strength Mg²⁺ induces stronger localization of oxygen electron clouds than Sr²⁺. Partial density of states (PDOS) analysis reveals that Mg 3p does not hybridize with O 2p, whereas Sr 4p exhibits p-p hybridization with O 2p, providing an electronic-level explanation for the contrasting network constraints exerted by the two cations. Coordination number analysis further distinguishes their structural roles: Mg²⁺ acts as a network intermediate (CN = 4-5), while Ca²⁺ and Sr²⁺ behave as typical network modifiers. Under equimolar Mg/Ca mixing conditions, the nonlinear effect is most pronounced, with the NBO fraction reaching 28.6%, the free volume fraction expanding from ~1.04% to ~2.75%, and the number of low-polymerization Qⁿ units increasing. These findings suggest that the competition among alkaline-earth cations in local coordination, orbital hybridization, and charge compensation is the fundamental origin of the nonlinear structural response, providing a theoretical basis for the compositional design of high-performance electronic substrate glasses.

A series of glass samples with a fixed total alkaline-earth oxide content (26.17%) and varying Mg/Ca/Sr ratios were prepared by conventional melt-quenching at 1650 ℃ and subsequently annealed at 600 ℃. The local coordination environments of ²⁹Si and oxygen atoms were characterized using magic-angle spinning nuclear magnetic resonance (MAS-NMR) and X-ray photoelectron spectroscopy (XPS). To investigate the atomic-scale evolution of the network, ab initio molecular dynamics (AIMD) simulations were conducted using the CP2K package. The 321-atom models used the PBE exchange-correlation functional and Goedecker-Teter-Hutter (GTH) pseudopotentials. Structural descriptors-including radial distribution functions (RDFs), bond-angle distributions, coordination-number curves n(r) obtained from RDF integration, Qⁿ distributions, oxygen coordination ratios, and free volume fractions-were extracted from the optimized configurations to quantify short- and medium-range network structures. Electronic-structure analyses, including electron localization function (ELF) and partial density of states (PDOS), were performed to characterize bond ionicity and orbital hybridization.

The results indicated that while the short-range structures of Si-O, Al-O, and B-O units remained highly stable across compositions, medium-range topological connectivity was strongly influenced by the MAEE. ELF analysis revealed that in the Mg-rich N3 composition, the bond-region ELF is 0.503, lower than 0.524 in the Sr-rich N5 composition (ΔELF = -0.021). This directly confirmed that high-field-strength Mg²⁺ exerted stronger polarization and localization on oxygen electron clouds, resulting in more ionic Mg-O bonds. PDOS analysis further showed that Mg 3p was buried at -37.6 eV with an O 2p energy separation exceeding 33 eV (no hybridization), whereas Sr 4p lay at -14.3 eV with an O 2p separation of only ~11 eV (p-p hybridization). The weaker electrostatic constraint from Sr²⁺ allowed O 2p orbitals to shift toward the Fermi level, consistent with the ELF findings. In the CaO-SrO substitution sequence, larger Sr²⁺ ions acted primarily as long-range charge compensators for [AlO₄]^- units, increasing the BO fraction to 74.8% in sample N2. In Mg-rich systems, high-field-strength Mg²⁺ induced more NBOs; however, the short and strong Mg-O ionic bonds locally constrained the network. Coordination number analysis from RDF integration clearly distinguished the structural roles: Mg²⁺ exhibited a CN distribution of 4-5 (network intermediate behavior), while Ca²⁺ and Sr²⁺displayed higher and broader CN distributions (typical network modifier behavior), resolving the ambiguity in bond-angle distribution differentiation. Notably, when MgO and CaO coexist in equimolar proportions within a high-SrO matrix (sample N6), pronounced nonlinear variations emerged: the NBO content reached 28.6%, and the free volume fraction, quantitatively evaluated by probe-sphere scanning (probe radius 1.2 Å, grid spacing 0.1 Å), increased from ~1.04% (N3, Mg-rich) to ~2.75% (N5, Sr-rich). This expansion was primarily attributed to the dilation of modifier coordination environments rather than to rupture of the network-former framework.

This study systematically elucidates the regulating role of alkaline-earth cation ratios on the network structure of alkali-free aluminosilicate glasses in the MgO-CaO-SrO ternary system by combining NMR and XPS characterization with AIMD simulations. The short-range structural units (Si-O, Al-O, B-O) exhibit high stability against compositional variation (Si-O CN = 4.00 ± 0.01; B-O predominantly [BO₃] with average CN = 3.08 ± 0.12), whereas the medium-range topological structure exhibits a pronounced nonlinear MAEE. High-field-strength Mg²⁺ promotes NBO formation with strong electron localization (ELF = 0.503) and no Mg 3p/O 2p hybridization, resulting in highly ionic Mg-O bonds that locally constrain the network. In contrast, larger-radius Sr²⁺ preferentially compensates [AlO₄]^- charges with weaker electron localization (ELF = 0.524) and exhibits Sr 4p/O 2p p-p hybridization (energy separation ~11 eV), facilitating network condensation. Coordination number analysis further differentiates the structural roles: Mg²⁺ functions as a network intermediate (CN = 4-5) and Ca²⁺/Sr²⁺ function as network modifiers (higher and broader CN distributions). Under equimolar Mg/Ca mixing (sample N6), the nonlinear response is most pronounced, with the NBO fraction reaching 28.6% and the free volume fraction expanding from ~1.04% (N3) to ~2.75% (N5), accompanied by an increase in low-polymerization Qⁿ units. These findings demonstrate that competitive coordination, orbital hybridization, and charge-compensation behaviors among alkaline-earth cations are the fundamental origin of the nonlinear structural response. The close agreement between experimental observations and simulation trends confirms the reliability of AIMD for resolving the structural evolution of complex glass networks and provides a theoretical basis for the compositional design of high-performance electronic substrate glasses.

Float glass is a fundamental material for architecture, automotive applications, electronic displays and photovoltaics, and its quality stability directly affects downstream processing efficiency and end-use performance. In continuous float glass production, defects such as bubbles, knots, stones and point defects reduce product grade, increase cutting losses and rework costs, and disturb stable operation. Existing online defect inspection systems usually identify defects after they have already formed, so they cannot provide sufficient support for feed-forward adjustment of furnace thermal conditions, combustion state, glass level or tin-bath atmosphere. The melting and forming process is a highly nonlinear, strongly coupled, and long-lag industrial thermal process. Process disturbances are transmitted through the temperature field, pressure field, atmosphere field and glass flow field, and their influence may appear at the inspection end only after tens of minutes or longer. Therefore, this study focuses on short-term prediction of four types of defects with 10 levels in float glass and proposes a lag-aware variable-coupled TimeMixer model, named LVC-TimeMixer, to establish a dynamic mapping from historical process states and defect sequences to future defect trends.

Historical records from a float glass production line were used to construct the prediction dataset. The input data consisted of 31 process variables covering furnace thermal state, pressure and liquid level, combustion and atmosphere, tin-bath protective gas, feeding and control settings, together with 40 historical defect labels corresponding to Bubble, Knot, Stone and PDefect at ten levels. The raw 10 s records were sorted by timestamp and resampled to a 1 min interval. Window averaging was applied to process variables to suppress high-frequency noise, while defect labels were aggregated by counts in each window; short missing values were treated using forward filling and linear interpolation. Supervised samples were generated by a sliding-window strategy, with a 360 min historical input window and a 30 min prediction horizon. On the basis of the original TimeMixer framework, LVC-TimeMixer introduces a lag-aware encoder and a variable-coupled mixer before the multiscale mixing backbone. The lag-aware encoder explicitly extracts delayed representations under candidate lag scales of 5, 10, 30, 60, 120 min and 240 min and uses a gating mechanism to adaptively select effective lag information. The variable-coupled mixer groups variables according to process semantics, including temperature, pressure, flow, atmosphere and historical defects, and then learns intra-group representations and inter-group coupling relationships. The encoded representation is subsequently fed into the TimeMixer backbone, where multiscale decomposition, Past-Decomposable-Mixing and Future-Multipredictor-Mixing are used to predict the multi-step defect sequence.

The proposed model was compared with DLinear, PatchTST, iTransformer, the original TimeMixer and WPMixer under the same data split, input window, prediction horizon and evaluation metrics. The results show that LVC-TimeMixer achieved the best overall performance in MAE, RMSE, R² and 30 min cumulative defect MAE, with corresponding values of 10.525, 30.368, 0.616 and 5 194.10, respectively. Although its sMAPE and severe-defect F1 were not the best among all models, they remained close to the leading level, indicating that the proposed lag-aware and variable-coupled structures improve trend fitting and cumulative risk estimation while maintaining competitive severe-defect warning capability. At prediction horizons of 5, 10, 15, 20 min and 30 min, LVC-TimeMixer consistently maintained the lowest MAE and a relatively high R², demonstrating stable short- and medium-term prediction capabilities.

For different defect categories, the optimal model varied because Bubble, Knot, Stone and PDefect have different temporal patterns and process response mechanisms. LVC-TimeMixer did not achieve the best MAE for every single defect type, but its errors for all four defect categories were close to those of the corresponding optimal models, and its overall defect prediction and 30 min cumulative risk estimation were superior. Rolling prediction curves and predicted-vs.-true scatter distributions further indicate that LVC-TimeMixer can better track the rise, fall and local peaks of total defects on chronological test data. Ablation experiments confirm that the lag-aware encoder improves prediction by aligning process disturbances with delayed defect responses, whereas the variable-coupled mixer used alone may be limited when lag alignment is absent. When both modules were introduced simultaneously, the MAE and 30 min cumulative defect MAE were further reduced by 21.22% and 16.60%, respectively, compared with TimeMixer-Lag, proving the synergistic effect between lag-aware encoding and variable-group coupling. Engineering evaluation showed that the average and maximum inference times for a single prediction window were 11.6 ms and 21.3 ms, respectively, on a computing platform equipped with custom 2 × 24 GB VRAM NVIDIA GeForce RTX 4090 GPUs, which is far below the 1 min rolling warning threshold in industrial applications.

In this study, an LVC-TimeMixer model was proposed for short-term multiclass defect prediction in the float glass melting-forming process. By embedding a lag-aware encoder and a variable-coupled mixer into the TimeMixer multiscale trend-fluctuation modeling framework, the model explicitly describes both the delayed relationship between process disturbances and defect responses and the coupling relationship among temperature, pressure, flow, atmosphere and historical defect variables. The comparative experiments, prediction-curve analysis and ablation study demonstrate that the proposed model improves overall prediction accuracy, cumulative risk estimation and prediction stability. The model can provide data-driven support for rolling defect early warning, furnace operating-condition optimization and intelligent quality control in float glass production. However, further research is still required to improve generalization under extreme operating conditions, to verify long-term real-time deployment, and to integrate synchronized stripe-image diagnosis or other multi-source visual information.

As a critical reinforcing material, high-modulus glass has experienced a rapidly growing demand in aerospace, wind power and other high-tech industries. This trend makes the development of novel high-performance, high-modulus glass formulations an urgent and significant task for materials science.

Machine learning has emerged as a transformative new research paradigm following traditional trial-and-error experiments and computer simulations. It offers remarkable advantages in efficiency, accuracy, and low energy consumption, and has been widely used to predict various glass properties.

However, purely data-driven machine learning models face severe performance bottlenecks when predicting glass Young's modulus (YM), showing significantly lower accuracy than models for other glass properties. This limitation mainly stems from two factors: poor data consistency in public databases due to the sensitivity of YM to preparation-related thermal history and testing conditions, and the lack of effective strategies to eliminate abnormal samples, which leads to model overfitting and limited generalization and extrapolation capabilities.

Liu and co-workers showed that pure data-driven models face three major contradictions in materials science, and proposed using data governance and domain knowledge embedding to improve model performance. Data quality is decisive for model performance, and combining anomaly detection with a divide-and-conquer strategy can yield accurate and interpretable models.

The Makishima-Mackenzie (MM) model is a classic semi-empirical model for predicting the YM of oxide glass with clear physical significance, but it exhibits a well-documented systematic underestimation of actual YM values. Residual modeling has been proven to be an effective approach to address the deficiencies of physical models. Therefore, this study aims to develop a physics-informed machine learning (PIML) model by integrating the MM model with machine learning to overcome the limitations of pure data-driven models.

The original dataset was constructed from the SciGlass database, which contained 2861 samples, with 15 core oxides as features and YM and density as target variables. Basic data cleaning was first performed to remove samples with incomplete compositions, missing values, zero values, negative values, and duplicate formulations.

Five pure data-driven models, namely Gradient Boosting Machine (GBM), Random Forest (RF), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN) and Support Vector Regression (SVR), were trained on the original dataset. All models were optimized using Bayesian optimization and five-fold cross-validation, with the dataset split into training and test sets in an 8:2 ratio.

Subsequently, the MM model was used to calculate the theoretical YM values for all the samples. Based on the physical principle that the MM model systematically underestimates glass YM, samples with negative residuals (measured YM minus MM-calculated YM) were eliminated. This procedure yielded a high-quality dataset of 2463 samples constrained by physical principles.

The GBM model, which exhibited the best performance among the three pure data-driven models, was selected for constructing the PIML model. Instead of directly predicting YM, the PIML model used oxide compositions as input features and the MM model residuals as the prediction target, and was trained on the high-quality dataset. The final YM prediction was obtained by adding the predicted residual to the MM-calculated value.

Model performance was evaluated using three comprehensive metrics: the coefficient of determination (R²), mean squared error (MSE), and median absolute error (MedAE). SHapley Additive exPlanations (SHAP) analysis and physicochemical-descriptor modeling were employed to reveal the error mechanism of the MM model. Extrapolation capability was tested using 29 samples with Li₂O, Y₂O₃, La₂O₃ and ZrO₂ contents outside the training set range.

The pure data-driven GBM model achieved a test-set R² of 0.910 on the original dataset, representing the best performance among the three models. However, its performance was still substantially limited by the abnormal samples present in the dataset.

In contrast, the developed GBM-PIML model trained on the physically constrained high-quality dataset achieved a markedly improved test set R² of 0.962. Compared with the pure GBM model, its MedAE was reduced by approximately 40% and its MSE was reduced by approximately 70%. These results demonstrated that the physical-constraint-based data cleaning effectively removed misleading abnormal samples, and the residual learning strategy reduced the learning difficulty of the model by allowing the model to learn the systematic error pattern of the MM model rather than the entire complex composition-YM relation.

SHAP analysis revealed that increases in Y₂O₃, La₂O₃, MgO, and Li₂O contents significantly increased the MM model residuals. This result was attributed to the fact that the MM model treated glass as a homogeneous system and ignored two key effects: the accumulation of non-bridging oxygen by high-field-strength modifiers and the interaction between rare earth metals and aluminum ions.

A physicochemical descriptor model was further constructed using four descriptors (Ma, ACFS, Re/Al, IMO/Si) that characterize internal glass structure and oxide interactions and achieved a test set R² of 0.932. SHAP analysis of this model confirmed that a lower degree of network polymerization, higher average cation field strength, higher Re/Al ratio and higher IMO/Si ratio all contributed to larger MM model residuals, which was consistent with the underlying physical mechanism.

Extrapolation tests showed that the GBM-PIML model exhibited significantly lower prediction errors than the pure GBM model in the high-modulus region. Most importantly, its error did not increase sharply as YM increased, indicating strong extrapolation stability.

This study constructs a residual-learning-based PIML model by integrating the MM physics-based model with the GBM machine-learning algorithm. The model achieves a test set R² of 0.962, a MedAE of 0.77, and an MSE of 7.04, and demonstrates superior prediction accuracy, robustness and generalization capability compared to conventional purely data-driven models trained on the same original dataset.

The combination of SHAP analysis and physicochemical descriptor modeling provides deep insight into the error mechanism of the MM model. The results reveal that the systematic error of the MM model mainly originates from its neglect of the effects of high-field-strength ions and oxide interactions on glass YM.

Extrapolation tests confirm that the incorporation of physical prior knowledge significantly enhances the model's extrapolation ability, especially in the high-modulus region. The proposed PIML model provides a new paradigm for the rapid development of high-modulus glass formulations with high prediction accuracy, interpretability, and strong extrapolation capability.

The design of the stress-applying part (SAP) in polarization-maintaining fibers (PMF) is a typical multi-variable, multi-objective optimization problem. The coupled effects of SAP diameter, B₂O₃ molar fraction and SAP edge-to-core distance determine attenuation, polarization-maintaining capability, mechanical reliability, and radiation resistance. Conventional trial-and-error design is therefore inefficient, especially when multiple performance constraints must be satisfied simultaneously.

Based on 500 experimental datasets, predictive models were established for eight performance indicators, including attenuation, post-irradiation loss, tensile strength, nd value, extinction ratio, temperature-induced extinction-ratio variation, beat length, and grinding crack probability. Three ensemble learning algorithms, i.e., random forest (RF), XGBoost, and gradient boosting regressor (GBR), were systematically compared via 5-fold cross-validation. SHAP analysis was employed to reveal the dominant effects of design variables. Furthermore, a large language model (LLM) was introduced as a natural-language interface and explanation layer to parse user requirements, diagnose infeasible constraints and provide minimum-relaxation suggestions.

The optimal models achieved R² values ranging from 0.76 to 0.99 with an average of 0.906, indicating that the nonlinear mapping between SAP parameters and performance metrics can be reliably captured. SHAP analysis revealed that the edge distance dominated attenuation-related indices, the SAP diameter governed mechanical and polarization-related properties, and the B₂O₃ molar fraction exerted the strongest effect on grinding crack probability. For 30 natural-language test cases, the LLM achieved 100% accuracy in indicator recognition and inequality-direction determination, and 85.0% exact-value accuracy. Across seven representative design scenarios, the proposed framework reduced the expected number of trial experiments by 91% on average and by up to 99.8% in the four-constraint scenario. When no feasible solution existed, the system could identify the bottleneck constraint and quantify the minimum relaxation required to recover feasibility.

A collaborative ML-LLM framework was developed for the inverse design of PMF SAPs. The method integrates quantitative prediction, mechanism-oriented interpretation and natural-language-driven interaction, thereby converting conventional trial-and-error design into an efficient data-driven screening process. This approach provides a practical pathway for intelligent design of polarization-maintaining fibers and may also be extended to other inorganic non-metallic materials with strongly coupled process-property relationships.

Durability is a fundamental property of concrete, and research on concrete durability differs markedly among service environments. The deterioration mechanisms of concrete materials encompass a complex interplay of physicochemical processes, including carbonation, freeze-thaw damage, wet-dry cycling, sulfate erosion and chloride ion erosion. Considering the coupled erosion mechanism of chloride ingress and freeze-thaw cycling that persistently affect concrete structures in frigid coastal regions of northern China, the non-steady-state transport characteristics and spatiotemporal distribution patterns of chloride ions within concrete media are critical parameters for durability assessment in civil engineering applications. The development of predictive models for chloride ion transport in concrete matrices has therefore attracted increasing research attention. Conventional chloride ion transport models often ignore the dynamic effects of low-temperature environments on the micro-pore structure of concrete, resulting in insufficient model prediction accuracy. In this study, the microscopic mechanism of pore structure and chloride ion transport in concrete in low-temperature environments was studied, and a chloride ion diffusion model with higher engineering applicability was constructed.

The chloride ion transport model suitable for low-temperature environments was established in three steps. The first step was to determine the evolution mechanism of concrete pore structure. Under the combined effects of cryogenic conditions and saline solution infiltration, crystals tended to form within the concrete matrix. These crystals filled concrete capillary pores, densified the matrix, and helped improve concrete impermeability. Crystalline particles could create stress differences around pores, promoting pore expansion and crack formation, thereby reducing the durability of concrete. The second step was to construct a pore structure change model. The pore structure change model included two parts: a time-varying model of temperature field was constructed based on trigonometric functions. and a dynamic evolution equation of porosity considering the coupling effect of temperature fluctuation on crystal filling and microcrack propagation was established to achieve the quantitative characterization of multiphysics response of pore structure. The third step was to construct a chloride ion transport model considering age attenuation, multi-ion coupling, damage to the critical pore size and most probable pore size. The Bruggeman model is combined with the dynamic porosity model to modify the chloride diffusion coefficient. Based on the mathematical and physical nature of Fick's second diffusion law, a chloride ion transport model with time-varying parameters was constructed.

The microstructure evolution of pores had a significant influence on the transport characteristics and spatial distribution of chloride ions during chloride-ion erosion of structures at low temperature. Compared with the traditional diffusion model, the results showed that the chloride ion penetration depth and concentration predicted by the new model established in this study were more consistent with the exposure test data. The evolution of pore structure of concrete under low temperature could be divided into two characteristic stages: crystallization filling stage and micro-crack propagation stage. In the chloride ion transport mechanism, the enhancement effect of microcrack propagation on the effective chloride ion diffusion coefficient was significantly higher than that of crystal filling at low temperature. This phenomenon revealed the key controlling effect of crack network connectivity on ion migration channels.

This study constructs a modified model that accounts for the influence of concrete pore changes on chloride ion diffusion under time-varying temperature in northern coastal areas. Comparison with exposure test data verifies the applicability of the model, which is then used to study the transport behavior of chloride ions under the influence of temperature, tortuosity and initial porosity of concrete through finite element analysis. The main conclusions are as follows: Temperature has an important influence on chloride ion transport. Regulated by the crystallization-cracking mechanism, the temperature decreases from 273.15 K to 253.15 K, and the chloride ion concentration in concrete decreases first and then increases. This effect is more significant with time, and the effect of crystallization-cracking mechanism on chloride ion transport is most obvious at the depth of 9 mm. As a dynamic driving factor, the time-varying temperature has a stronger accelerating effect on chloride ion transport than the constant temperature condition. Under temperature cycling, chloride ions achieve faster and deeper migration through the crack network generated by freeze-thaw damage, and its peak concentration can reach 2.46 times that under constant temperature conditions. As an initial structural characteristic of concrete, the average particle size of aggregate is closely related to the tortuosity of concrete pore network and chloride ion diffusion capacity. The chloride ion transport process in concrete with different aggregate sizes is simulated under time-varying temperature conditions. A larger aggregate size, corresponds to lower tortuosity of the internal concrete pores, and the chloride ion diffusion coefficient of the concrete with an average aggregate size of 20 mm is 1.38 times that of the concrete with an average aggregate size of 16 mm. Affected by differences in the initial pore structure, under the cumulative effect of long-term chloride ion erosion, the difference of chloride ion concentration in concrete with different aggregate sizes gradually increases as the erosion duration increases. The initial porosity of concrete is closely related to the chloride ion transport behavior. The higher the initial porosity, the higher the chloride ion concentration, and as the erosion time increases, the acceleration effect of large porosity on chloride ion transport becomes more significant; in practical engineering applications, when the initial porosity of concrete increases from 0.10 to 0.15, the corrosion time of steel bars at a depth of 20 mm in the structure is shortened by 12.42 years.

The application of nanomaterials in cement-based composites has attracted considerable research interest in recent years, because of their ability to enhance mechanical properties such as compressive, flexural, and fracture strength. Among these materials, nanocellulose, a bio-derived nanomaterial obtained from abundant renewable resources and characterized by excellent properties, has considerable potential for use in cement composites. Its surface functional groups can act as nucleation sites for calcium silicate hydrate (C-S-H) gel, leading to the formation of denser hydration products and refined pore structures. Furthermore, nanocellulose inhibits microcrack propagation through nano-filling and bridging effects, thereby synergistically improving the mechanical performance of cement-based materials. Despite these advances, the interfacial bonding behavior between nanocellulose and C-S-H gel at the nanoscale is not well understood because of limitations in conventional macroscopic experiments and microscopic characterization techniques. The reinforcing effect of nanocellulose has been proposed to arise primarily from a dual micromechanism of "short-circuit diffusion and bridging crack resistance". However, direct atomistic evidence supporting this hypothesis is lacking. In particular, the influence of nanocellulose surface functionalization (e.g., carboxylation) on interfacial interactions has not been systematically investigated. In this study, molecular dynamics (MD) simulations were used to investigate the interfacial structure, interaction energy, hydrogen bonding evolution, and failure behaviors of nanocellulose/C-S-H composites. A novel embedded composite model was constructed using a Monte Carlo-based strategy. The effects of hydroxylated and carboxylated nanocellulose on interfacial binding and mechanical performance were systematically compared.

The C-S-H gel model with a chemical composition of (CaO)_1.65(SiO₂)(H₂O)_1.75 was constructed following the realistic disordered structure proposed by Pellenq et al., which matches the experimental density (~2.6 g/cm³) and Ca/Si ratio (1.6-1.8). The modeling process started from the 11 Å tobermorite crystal structure, followed by random removal of bridging silicate tetrahedra, water saturation at 298 K, and subsequent energy minimization and NPT dynamic relaxation (298 K, 120 ps, 1 fs timestep) using the COMPASS force field. C-S-H models with different Ca/Si ratios (0.75, 1.31, 1.48, and 1.67) were also constructed by controlling the removal of silicate tetrahedra. Amorphous cellulose models (both hydroxylated and carboxylated) were built using cellobiose as the repeating unit. A three-dimensional periodic amorphous cellulose model (43.0 Å × 43.0 Å × 22.0 Å) was constructed by randomly packing five cellulose chains using the Grand Canonical Monte Carlo (GCMC) method, followed by NVT relaxation (298 K, 100 ps, 1 fs timestep). The final density of 1.32 g/cm³ agreed well with experimental values. The C-S-H/cellulose composite model was constructed by inserting the amorphous cellulose box into the interlayer of C-S-H, with a 5 Å vacuum layer to avoid initial atomic overlap. The system was first energy-minimized using the smart algorithm (energy tolerance 1.0×10^-4 kcal/mol, force tolerance 0.001 kcal/mol/Å), followed by NVT equilibration (298 K, 100 ps, 1 fs timestep). Uniaxial tensile simulations were performed along X, Y, and Z directions to obtain stress-strain curves, Young's modulus and tensile strength. Interfacial interaction energy was calculated as E_inter = E_{_total}-(E_{_C-S-H} + E_{_CNF}). Hydrogen bonding, radial distribution functions (RDF) and mean squared displacement (MSD) were also analyzed.

With increasing Ca/Si ratio from 0.75 to 1.67, the degree of polymerization of silicate chains decreased systematically, as evidenced by the decrease in Q₂ content from 100% to 20%. The C-S-H model with Ca/Si = 1.67 yielded a density of 2.44 g/cm³ and Q_n distribution (Q₀ = 11.6%, Q₁ = 65.12%, Q₂ = 20%), which closely matched the real molecular structure of C-S-H gel. Uniaxial tensile simulations revealed that Young's modulus decreased with increasing Ca/Si ratio in all three directions (X, Y, Z). For Ca/Si = 1.67, Young's modulus values ranged between 10-30 GPa, consistent with nanoindentation experimental results. The Y-direction exhibited the highest modulus due to the dominant contribution of silicate chains, while the X and Z directions were governed by the fractured hydrogen bond network. The interfacial interaction energies for both hydroxylated and carboxylated nanocellulose with C-S-H were negative, indicating spontaneous adsorption and thermodynamically stable interfaces. The absolute value of interfacial interaction energy for carboxylated nanocellulose/C-S-H (-1.70×10² kJ/mol) was significantly larger than that for hydroxylated nanocellulose/C-S-H (-0.73×10² kJ/mol), demonstrating stronger spontaneous adsorption and tighter interfacial bonding after carboxylation. The interaction energy mainly consisted of van der Waals and electrostatic contributions, with electrostatic energy accounting for over 90% of the total adsorption energy. Hydrogen bond analysis showed that in the C-S-H/carboxylated nanocellulose composite model, 56.1 hydrogen bonds were formed in the interfacial region, corresponding to a hydrogen bond density of 3.12 bonds/nm². In contrast, the hydroxylated nanocellulose/C-S-H interface gave only 25.7 hydrogen bonds with a density of 1.43 bonds/nm². The significantly higher hydrogen bond density after carboxylation strongly enhanced interfacial binding. RDF analysis revealed characteristic peaks at 0.97 Å and 2.05 Å for oxygen and hydrogen atoms in C-S-H, with atomic distances smaller than the hydrogen bond cutoff distance of 2.5 Å. The carboxylated system exhibited more pronounced peaks, indicating stronger bonding ability of carboxyl oxygen with interfacial hydrogen. MSD analysis showed that atoms at the interface of the carboxylated nanocellulose/C-S-H composite had lower MSD values than those in the hydroxylated system, indicating stronger atomic constraints and lower motional freedom. This enhanced interfacial stability enabled more effective stress transfer from the matrix to the reinforcing phase.

In this work, molecular dynamics simulations were used to investigate the interfacial interaction and reinforcement mechanism between nanocellulose and C-S-H. The results showed that the C-S-H model with a Ca/Si ratio of 1.67 constructed by the Monte Carlo method yielded a density of 2.44 g/cm³ and a Q_n distribution (Q₀ = 11.6%, Q₁ = 65.12%, Q₂ = 20%) that closely matches actual C-S-H gel. As the Ca/Si ratio increases from 0.75 to 1.67, the degree of polymerization of silicate chains decreases, and Young's modulus in all three directions decreases accordingly. Spontaneous adsorption occurs at the nanocellulose/C-S-H interface, where functional groups on the nanocellulose surface form hydrogen bonds to promote interfacial bonding. The number of hydrogen bonds formed at the interface of carboxylated nanocellulose/C-S-H (56.1) was significantly greater than that of hydroxylated nanocellulose/C-S-H (25.7). Both types of nanocellulose reinforce C-S-H, but the interfacial interaction energy of carboxylated nanocellulose/C-S-H (approximately -1.70×10² kJ/mol) is higher than that of hydroxylated nanocellulose, with electrostatic energy contributing over 90% of the total adsorption energy. These findings demonstrate that carboxylation modification significantly enhances interfacial bonding, providing an atomistic-scale theoretical basis for designing high-performance cement-based composites.

Ultra-high performance concrete (UHPC) has emerged as a critical material for achieving structural lightweight design and enhanced longevity in civil engineering due to its exceptional mechanical properties and durability. The optimization of UHPC performance relies on particle gradation design for constructing dense packing systems and interfacial transition zone (ITZ) regulation to enhance matrix continuity. However, traditional UHPC formulations depend on resource-intensive materials such as river sand and silica fume, while existing particle packing theories predominantly assume ideal spherical particles, lacking systematic understanding of the coupling mechanisms associated with natural aggregate complex morphological characteristics. Volcanic rock, characterized by rich active substances and irregular angular morphology, offers a novel material pathway for UHPC performance regulation through multi-scale morphological control when processed into aggregates (replacing standard sand) and mineral admixtures (volcanic rock powder replacing cement).

The study systematically investigated the physicochemical synergistic enhancement mechanisms of volcanic rock materials in UHPC through comprehensive experimental analysis. Volcanic rock aggregates (VRA) with varying replacement ratios (0%-100%) of standard sand and volcanic rock powder (VRP) with different substitution levels (0%-30%) of cement were incorporated into UHPC mixtures. The modified Andreasen-Andersen model guided the mix design optimization. Fresh properties were evaluated through flow spread tests and wet packing density measurements according to the established method. Mechanical performance was assessed via compressive and flexural strength tests at 3, 7 d, and 28 d. Accelerated ASR expansion was measured using mortar bar method. Three-dimensional structural analysis using X-ray computed tomography (CT) characterized macropore structure and fiber orientation distribution. Hydration processes were monitored using isothermal calorimetry, while microstructural features were examined through scanning electron microscopy (SEM) and nanoindentation testing.

The workability and wet packing density of fresh UHPC exhibited an initial increase followed by a decrease with increasing VRA replacement ratios, achieving optimal values at 50% replacement. Flow spread increased from 122 mm to 133.5 mm before declining to 128.5 mm, while wet packing density improved from 0.803 to 0.819 before decreasing to 0.808. VRP substitution demonstrated a monotonic increasing trend in flowability, with wet packing density peaking at 10% replacement. This phenomenon was attributed to the friction network regulation mechanism of "angular-rounded" particle interactions and particle gradation optimization by volcanic rock powder.

Mechanical performance analysis revealed that 75% VRA replacement maximized flexural strength across all curing ages, while 20% VRP substitution yielded optimal 28 d compressive strength. X-ray CT analysis demonstrated that 50% VRA replacement minimized total porosity, with large pores (10⁷-10⁸ μm³) systematically decreasing as VRA content increased. The angular morphology of volcanic rock aggregates promoted preferential horizontal alignment of steel fibers, with significant clustering in low-angle regions (0°-30°), thereby substantially enhancing UHPC flexural performance. VRP optimized pore distribution structure through pozzolanic reactions filling microscopic pores at appropriate replacement levels, achieving a dynamic balance between secondary hydration benefits and cement dilution effects. Nanoindentation results confirmed that VRA-matrix interfacial transition zones exhibited superior mechanical properties compared to those of standard sand interfaces, with elastic modulus improvements of approximately 49.7%.

Calorimetric analysis revealed that VRA incorporation increased 3 d cumulative heat release compared to standard sand groups, attributed to surface active substances and enhanced interfacial contact promoting early cement hydration. VRP exhibited delayed hydration kinetics with peak heat release occurring at later ages, with 20% replacement achieving the highest 3 d cumulative heat release due to optimized pozzolanic reaction contributions, while higher dosages (30%) inhibited hydration processes due to lower reactivity of VRP and dilution effect.

This study established that volcanic rock materials effectively enhance UHPC performance through physicochemical synergistic mechanisms, with optimal performance achieved at 50%-75% VRA replacement and 20% VRP substitution. The angular morphology of volcanic rock aggregates improved fiber orientation distribution and mechanical interlocking with the cement matrix, while volcanic rock powder optimized particle packing efficiency and promoted secondary hydration reactions. The enhancement mechanisms originate from three synergistic factors: superior intrinsic mechanical properties comparable to standard sand, angular particle morphology creating three-dimensional mechanical interlocking structures that optimize packing efficiency, and surface active components promoting pozzolanic reactions that densify interfacial microstructure. These findings provide experimental evidence for extending particle packing theory beyond conventional spherical particle assumptions, establishing critical relationships between “morphological characteristics-packing efficiency-interface performance” and macroscopic UHPC properties for complex-shaped aggregates design.

Since the late nineteenth century, railway construction has been a key driver of national development in China. The Yuehan Railway is one of the earliest modern railways in the country, playing an important role in advancing transportation infrastructure and regional economic growth. Over the past century, the concrete structures of the Yuehan Railway have been exposed to complex environmental actions, including high humidity, seasonal fluctuations in the Yangtze River water level, chemical ions, and sediment-induced abrasion. These factors have collectively accelerated surface deterioration and microstructural degradation. Recent decreases in river water levels have revealed the concrete remains of Xujia Peng Station, providing a rare opportunity to examine century-old concrete. This study conducts field sampling and laboratory analyses to characterize the composition and microstructure evolution of the relic concrete. Techniques including X-ray diffraction (XRD), thermogravimetric analysis (TG), scanning electron microscopy (SEM), backscattered electron imaging (BSE), and X-ray computed tomography (X-CT) are employed. The findings offer scientific support for the preservation of historic constructions and provide reference insights for durability design in cement materials.

Concrete samples were collected during the dry season from the exposed remains of the Xujia Peng Station section of the Yuehan Railway. Representative fragments were obtained from pile caps and bridge piers via manual chiseling, while intact cores were extracted from sleeper components. After drying and surface preparation, samples were processed for mechanical testing and microstructural analysis. Compressive strength and chloride ion penetration resistance were measured on φ 100 mm × h 100 mm and φ 100 mm × h 50 mm cylindrical specimens, respectively. Phase assemblages were quantified using (XRD) with Rietveld refinement, complemented by thermogravimetric analysis. Microstructural morphology, ITZ distribution, and elemental composition were examined via (SEM), backscattered electron imaging with image analysis (BSE/IA), and energy dispersive spectroscopy (EDS). Three-dimensional pore architecture was evaluated using high-resolution X-CT, enabling quantitative pore size distribution.

The sleeper concrete exhibited exceptional long-term stability, with an average compressive strength of 110.68 MPa and an electrical charge passed of 868 C, corresponding to a Q-IV chloride resistance grade. XRD and TG/DTG analyses revealed nearly complete hydration of clinker phases after nearly a century of service. C₃A and C₄AF were fully consumed, and only trace amounts of C₃S and C₂S remained. Due to prolonged exposure to air, pile cap and pier samples showed severe carbonation, with CaCO₃ mass fractions exceeding 20% and minimal portlandite. By contrast, sleeper samples exhibited limited carbonation. Quantitative BSE/IA demonstrated clear ITZ gradients in all samples. The ITZ thicknesses for pile cap, sleeper, and pier concretes were approximately 50, 30 μm, and 40 μm, respectively. ITZ regions consistently exhibited elevated porosity and reduced hydration product content near aggregate surfaces. The gradient of hydration product volume fraction stabilized beyond the ITZ boundary, indicating a distinct microstructural transition from porous interface zones to dense bulk matrix. SEM/EDS analyses revealed dense C-S-H gel, AFt/AFm products, localized carbonate phases, and well-preserved matrix integrity in the sleeper samples. While microcracks existed near aggregate boundaries, the overall matrix remained chemically stable. X-CT imaging showed a predominantly disconnected pore system consisting of fine capillary pores and a small number of large voids. The critical pore volume of the sleeper concrete was 0.1 mm³, above which macropores dominated total porosity but did not form continuous pathways.

This study provides a comprehensive microstructural and performance assessment of century-old Yuehan Railway concrete. Sleeper concrete retained high compressive strength (110.68 MPa) and low chloride permeability (868 C), demonstrating excellent durability after nearly 100 years of service. Pile cap and pier concretes underwent significant carbonation, while sleeper concrete exhibited limited carbonation due to environmental shielding. ITZ thicknesses were approximately 50 μm (pile cap), 30 μm (sleeper), and 40 μm (pier), with higher porosity and microstructural heterogeneity compared to bulk matrix. X-CT analysis identified a critical pore volume of 0.1 mm³, indicating that large isolated pores govern overall porosity without forming permeable networks. Overall, the Yuehan Railway concrete demonstrates outstanding long-term mechanical and durability properties, offering valuable insights for cementitious material design.

Magnesium potassium phosphate cement (MKPC) exhibits rapid setting and hardening, high early strength, and excellent bonding performance, making it a promising material for the rapid repair of concrete structures. It is increasingly utilized in cold regions and deicing-salt environments (NaCl). However, the long-term performance of MKPC in NaCl environments remains unclear. Reported results differ regarding the performance evolution of MKPC in NaCl solutions. While some researchers reported that NaCl exposure increases material degradation, others have observed that, under specific conditions, the late-age strength can be maintained or even improved. This study aims to address the existing knowledge gap regarding the long-term performance of MKPC in NaCl environments.

Among the many factors affecting the durability of MKPC, the water-to-binder ratio (w/b) and NaCl concentration are particularly important. The w/b determines the extent of early reactions and the amount of unreacted residual particles, whereas the NaCl concentration controls ion migration and dissolution processes. In this study, the long-term evolution of MKPC with different w/b values at different NaCl concentrations was investigated.

In this study, the MKPC system was prepared with an MgO/KH₂PO₄ mass ratio of 2. Three w/b ratios (0.16, 0.22, and 0.24) were investigated, while the sand-to-binder ratio was kept constant at 0.8. Borax decahydrate was added as a retarder at 10% (mass fraction) of the MgO mass. After casting, the specimens were precured for 3 d and then cured for up to 360 d in air, DI water, 5% NaCl solution, and 15% NaCl solution, respectively.

To evaluate the long-term durability and volume stability, the linear dimensional change and compressive strength development were measured in different environments. Quantitative X-ray diffraction (QXRD) using the Rietveld method and thermogravimetric analysis (TG-DTG) were used to quantify the transformation of crystalline phases, amorphous content, and hydration products. The transition in hydration-product morphology and elemental distribution was observed by scanning electron microscopy (SEM) coupled with energy-dispersive spectroscopy (EDS). Finally, mercury intrusion porosimetry (MIP) was employed to analyze the pore structure evolution.

The volume stability results showed that the air-cured specimens underwent shrinkage and gradually stabilized after about 18 d, whereas the specimens immersed in pure water and NaCl solutions exhibited an initial shrinkage followed by expansion. The expansion in the 15% NaCl solution was slightly higher than that in 5% NaCl and DI water. The pH results showed that all immersion solutions remained above 7, indicating that MgO kept dissolving. However, the stable pH followed the order of DI water (10.2) > 5% NaCl (9.9) > 15% NaCl (9.6), suggesting that NaCl altered the reaction process.

The compressive strength of MKPC varied markedly under different conditions. For specimens cured in pure water, a decreasing trend is observed after 28 d of immersion, which is primarily attributed to the gradual dissolution of MgO and struvite-K. Conversely, specimens immersed in NaCl solutions continue to gain strength even after 28 d, indicating that the presence of NaCl promotes strength development of MKPC. After 360 d of immersion, specimens in the 15% NaCl solution reached compressive strengths ranging from 50.99 MPa to 51.34 MPa, higher than those in the 5% NaCl solution (42.32-50.07 MPa). Notably, the w/b = 0.22 specimen achieved the highest strength retention of 95.67% in the 15% NaCl environment.

QXRD analysis showed that struvite-K and MgO were the major mineral phases in all samples. Notably, a new hydrate, hazenite (KNaMg₂(PO₄)₂·14H₂O), was detected only in specimens cured in NaCl solutions, while it was absent in air- and DI-water-cured samples. With increasing curing age and NaCl concentration, the content of hazenite increased whereas the struvite-K content decreased, indicating a phase transformation from struvite-K to hazenite in the NaCl environment.

SEM observations showed that the air-cured specimens were mainly composed of relatively dense plate-like struvite-K, whereas the products in the DI-water-cured specimens were more needle-like. After curing for 28 d in NaCl solutions, plate/cluster-like crystals were formed. EDS results showed that the K: Na ratio in these crystals was approximately 1:1 (hazenite). Compared with the struvite-K, hazenite crystals were larger and filled pores. MIP results further showed that the porosity of the NaCl-cured samples was significantly lower than that of the DI-water-cured samples, and the pore structure was densified at later ages, with the porosity decreasing by about 6%.

The main conclusions of this paper are summarized as follows. The strength retention of the MKPC mortar samples increased from ~81% (in pure water) to about 95% at a w/b of 0.22 after storage in 15% NaCl solution for 360 d. A new phase, hazenite (KNaMg₂(PO₄)₂·14H₂O), increased with NaCl concentration and curing time. XRD/SEM results showed that hazenite (cluster/plate-like configuration) stemmed from struvite-K (needle-like shape) after incorporating Na⁺ and water, which contributed to a reduction in porosity (6%), leading to higher retention of compressive strength.

Sulfoaluminate cement-based grouting materials (SAGMs) have emerged as a high-performance solution in modern civil engineering, prized for their rapid setting, high early strength, and volumetric stability in applications ranging from rock reinforcement and concrete crack remediation to mine tunneling support. However, the intrinsic drawback of sulfoaluminate cement-its exceedingly rapid hydration kinetics and short setting times-severely restricts the operational window for grouting operations, challenging the feasibility of large-scale field applications. While chemical admixtures are conventionally employed to disperse cement particles, retard setting, and enhance fluidity to safeguard early-age workability, the distinct mineralogical composition of sulfoaluminate cement (characterized by ye'elimite and anhydrite) compared to ordinary Portland cement creates profound compatibility challenges. Conventional superplasticizers often fail to perform optimally, leading to issues such as flash setting or uncontrolled fluidity loss. Therefore, the rational design of molecularly tailored admixtures specifically compatible with the unique chemistry of SAGMs is of paramount importance. In this study, 3-mercaptopropionic acid (MPA) was utilized as a chain transfer agent to regulate molecular weight during the free radical polymerization of acrylic acid (AA), 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS), and acrylamide (AM), yielding a series of AA-AMPS-AM. The influence of MPA dosage on polymer architecture was systematically investigated, and the effects of molecular weight variation on the rheological properties, workability retention, and hydration mechanisms of SAGMs were evaluated, with a specific focus on dissolution-precipitation dynamics.

A suite of AA-AMPS-AM with tailored molecular weights was synthesized by modulating the MPA dosage during aqueous free radical polymerization. The successful synthesis and structural confirmation of these polymers were rigorously verified using Fourier-transform infrared spectroscopy (FT-IR), gel permeation chromatography (GPC), and proton nuclear magnetic resonance (¹H NMR). The performance of these polymers in SAGMs was quantitatively assessed according to Chinese National Standards: fluidity (GB/T 8077-2012), setting time (GB/T 1346-2011), and mechanical properties of hardened paste (GB/T 7897-2008). To elucidate the interaction mechanisms at the solid-liquid interface, adsorption behavior was probed using zeta potential measurements and total organic carbon (TOC) analysis. The modulation of the hydration process was investigated through the framework of dissolution-precipitation equilibrium, utilizing conductivity measurements and inductively coupled plasma (ICP) spectroscopy to track ionic concentration variations during mineral dissolution. The evolution of key hydration products was analyzed based on these ionic profiles and subsequent solid-phase characterization. Final hydration products were comprehensively identified using X-ray diffraction (XRD), thermogravimetric analysis (TG), laser particle size analysis, and scanning electron microscopy (SEM) for morphological assessment.

By maintaining a fixed monomer molar ratio of AA: AMPS: AM = 1:99:30 and adjusting the MPA dosage, water-reducing agents with systematically varied molecular weights were synthesized. Experimental results indicated that reducing the MPA dosage from 0.6% to 0.2% led to a controlled decrease in the molecular weight of AA-AMPS-AM. This molecular tuning significantly enhanced the fluidity of SAGMs; specifically, the incorporation of polymer-2, polymer-4, and polymer-6 increased the initial fluidity to 370, 365 mm, and 345 mm, respectively. Notably, the formulation containing polymer-2 exhibited exceptional fluidity retention, maintaining 100 mm of flowability even after 60 min. Adsorption analysis revealed that lower MPA dosages reduced the polymer's affinity for mineral surfaces, allowing a greater proportion to remain in the liquid phase to act as a steric dispersant. AA-AMPS-AM influenced the hydration process of the SAGMs, a lower MPA dosage strengthened the polymer's inhibitory effect on the dissolution of anhydrous calcium sulfate and calcium sulfoaluminate. This is attributed to AA-AMPS-AM effectively coating the mineral surfaces, creating a physical barrier that limits direct contact with the aqueous solution. Furthermore, AA-AMPS-AM reduced both the rate and total heat release of hydration, with the retarding effect becoming more pronounced at lower MPA contents. The polymers suppressed AFt formation and refined the particle size from 1600 nm to 200 nm, altering AFt morphology from long, slender rods to short, stubby rods. Conversely, lower molecular weight polymers (higher MPA dosage) promoted the formation of an aluminum-rich gel phase, facilitating particle aggregation on the external surface.

This study establishes a clear structure-performance relationship for polymer in SAGMs, demonstrating that molecular architecture-governed by MPA dosage-dictates their efficacy as water reducers and hydration modifiers. Reducing the MPA dosage yields polymers with lower molecular weights and narrower distributions, which optimizes slurry fluidity and effectively retards setting. Crucially, this reduction diminishes polymer adsorption onto cement grains, the resultant increase in free polymer concentration in the pore solution is pivotal for sustaining fluidity. Furthermore, the adsorbed polymer layer acts as a steric barrier that inhibits the dissolution of reactive minerals (anhydrite and ye'elimite), disrupting the dissolution-precipitation equilibrium. This leads to a delayed and modified crystallization of AFt, shifting its morphology from acicular to stubby prismatic crystals. Finally, polymerization conditions favoring lower MPA content promote the co-precipitation of Al(OH)₃ gel alongside the polymer, which encapsulates fine particles and creates a distinctly rough surface topography on the final product.

Natural gypsum is an important inorganic building and decorative material because it offers fire resistance, thermal insulation, acoustic comfort, low energy consumption during processing, and broad resource availability. Nevertheless, the practical upgrading of gypsum products is still constrained by their intrinsic brittleness, low load-bearing capacity, weak surface hardness, and poor resistance to abrasion or surface pulverization. These shortcomings are particularly unfavorable for high-value interior panels, decorative components, and other applications requiring both dimensional stability and long service life. Fiber reinforcement has been widely used to toughen gypsum, but fiber dispersion, reduced slurry workability, increased porosity, and cost or sustainability concerns can limit its application. Therefore, a simple, water-based polymer modification route is necessary to simultaneously improve workability, strength, surface integrity, and durability. In this study, natural gypsum was modified with macromolecular polymers, with the aim of screening an effective modifier and clarifying the relationship between polymer dosage, hydration behavior, microstructure, and macroscopic performance.

A series of polymer modifiers were first compared, including acrylic emulsion, water-based epoxy AB adhesive, styrene-acrylic emulsion, waterborne polyurethane (WPU), polyvinyl alcohol (PVA), polyacrylamide (PAM), and polyvinylpyrrolidone (PVP). Gypsum slurry was prepared at a gypsum-to-water ratio of 1.0: 0.6 according to the standard consistency requirement. For solid polymers, aqueous polymer solutions were prepared before mixing; for liquid emulsions or adhesives, the solid content was considered when preparing solutions of the required mass fraction. The polymer solution was added into the mixing vessel, gypsum powder was introduced, and the slurry was mixed, cast, vibrated, demolded after final setting, and cured under laboratory conditions. On the basis of preliminary screening, PVA was selected for dosage optimization, and gypsum composites containing 0.0%, 0.5%, 1.0%, 1.5%, and 2.0% (mass fraction) PVA were prepared. Workability was evaluated by spread diameter, initial setting time, and final setting time. Mechanical properties were characterized by flexural strength, compressive strength, and surface hardness, including wet-state tests after water immersion where applicable. Abrasion resistance was assessed using the pulverization rate after rubbing a 30 mm × 30 mm × 30 mm sample against 320-mesh abrasive paper under a constant load. The phase composition, hydration degree, and microstructure were analyzed by X-ray diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM), respectively.

The screening results showed clear differences among polymer types. PAM, acrylic emulsion, and styrene-acrylic emulsion caused severe loss of spreadability, probably because ionic interaction, flocculation, demulsification, and rapid viscosity growth produced agglomerated slurry systems. PVA, PVP, WPU, and water-based epoxy AB adhesive allowed the slurry to spread, but their effects on workability and setting behavior differed. Among them, PVA produced the most favorable mechanical improvement; at 0.5%, the flexural strength reached 2.3 MPa, 52.9% higher than that of the control sample. PVA was therefore selected as the main modifier.

With increasing PVA dosage, the spread diameter first increased and then decreased. A low PVA content improved particle dispersion through adsorption, electrostatic repulsion, and steric hindrance, whereas excessive PVA formed a three-dimensional network and bound free water through hydrogen bonding, reducing flowability. The initial setting time exhibited a similar first-increase and then decrease trend, while the final setting time decreased slightly overall. This behavior indicates that hydroxyl groups in PVA can interact with calcium ions and delay early nucleation at low dosage, but the water-binding and thickening effects become more pronounced at higher dosage.

The dry mechanical properties reached an optimum at 1.5% PVA. The flexural strength, compressive strength, and surface hardness were 5.2 MPa, 3.4 MPa, and 16.7 N/mm², respectively, representing increases of 344.4%, 84.9%, and 383.0% compared with the blank gypsum. The pulverization rate decreased sharply from 0.7979% to 0.0545%, demonstrating a remarkable enhancement in abrasion resistance. These improvements are attributed to the combined effects of polymer film formation, pore filling, interfacial bridging, and crystal regularization. PVA molecular chains contain abundant hydroxyl groups that can coordinate with calcium ions and form hydrogen bonds with calcium sulfate dihydrate (CaSO₄·2H₂O), thereby strengthening the intercrystalline interface and promoting efficient stress transfer. SEM images confirmed that appropriate PVA addition reduced pores, tightened crystal overlap, and made the fracture surface more compact. XRD patterns showed sharper characteristic peaks of CaSO₄·2H₂O, especially near 31.2°, indicating improved crystal regularity and orientation. TGA further showed that the mass loss associated with dehydration increased from 13.69% in the control sample to 15.89% at 1.5% PVA, suggesting a higher hydration degree and a larger amount of CaSO₄·2H₂O formed. However, when the PVA dosage increased to 2.0%, local polymer-rich agglomerates appeared, free-water diffusion was restricted, hydration and crystal interlocking were weakened, and the mechanical enhancement declined. Wet-state flexural strength, wet compressive strength, and water-immersed hardness changed only slightly, indicating that PVA mainly improves the dry strength and surface wear resistance, while its positive effect on wet-state performance is limited.

PVA is an effective water-soluble polymer modifier for natural gypsum. Compared with other polymer systems, it provides better compatibility between workability and reinforcement. The optimum dosage is 1.5%, at which flexural strength, compressive strength, surface hardness, and abrasion resistance are improved simultaneously. The strengthening mechanism can be summarized as a synergistic film-forming-filling, interfacial-bridging, and crystal-regularizing effect. Appropriate PVA promotes gypsum hydration, improves CaSO₄·2H₂O crystallization, reduces matrix porosity, and enhances crystal bonding. Excessive PVA, however, causes agglomeration, water-binding, increased porosity, and a diluted load-bearing skeleton, resulting in performance deterioration. This simple modification strategy provides a practical route and theoretical basis for preparing high-strength and wear-resistant gypsum-based composites.

Due to the large amount of old mortar attached to the surface of recycled coarse aggregate (RA), its water absorption capacity is significantly higher than that of natural coarse aggregate. Consequently, the water saturation degree (S_W) of RA plays a crucial role in influencing the microstructure and resistance to Cl^- erosion of recycled aggregate concrete (RAC). However, the specific effects of different saturation states on RAC performance remain controversial. Some studies suggest that water released from surface-saturated RA weakens the newly formed interfacial transition zone (ITZ), reducing resistance to chloride penetration, whereas others argue that its desorption behavior acts as an internal curing agent that enhances RAC durability. Furthermore, the influence of S_W on the microstructure under different water control schemes-namely, constant mixing water supply (labelled as S1) and constant total water supply (labelled as S2)-has yet to be fully elucidated. Therefore, this study aims to systematically investigate the effects of varying S_W of RA on chloride diffusion and binding behavior in RAC under different control schemes, and to elucidate the underlying microscopic mechanisms.

The RA used in this study had a particle size range of 4.8-31.5 mm, an apparent density of 2650 kg/m³, a water absorption rate of 5.4%, a crushing index of 16.6%, and an old mortar content of 36.7%. The fine aggregate was river sand with a fineness modulus of 2.0, a moisture content of 1.0%, and a mud content of 0.8%. Ordinary Portland cement 42.5 R with a specific surface area of 345 m²/kg and a density of 3.12 g/cm³ was used. A polycarboxylate superplasticizer with a solid content of 40% and a water reduction rate of 25% was added to control the workability of fresh concrete. Under both S1 and S2, the effective water-to-cement ratio (W_e/C) and the total water-to-cement ratio of RAC were maintained at 0.55. The S_W of RA was adjusted to 0%, 50%, 80%, and 100% by controlling the soaking time.

After curing for 28 d, the rapid chloride migration coefficient (D_RCM) of RAC was determined using the rapid chloride migration method. Cubic specimens were then immersed in a 5% NaCl solution for one-dimensional natural chloride ingress tests. At exposure durations of 60, 120, 180 d, and 240 d, the concentration profiles of free Cl^- and total Cl^- were measured using potentiometric titration. Based on Fick's second law of diffusion, the effective and apparent chloride diffusion coefficients (D_a and D_e) were calculated. In addition, after 28 d of curing, the distribution of unhydrated cement content and the porosity in the new ITZ were analyzed using backscattered electron imaging (BSE), while the pore size distribution of the new mortar was determined by mercury intrusion porosimetry (MIP). Furthermore, scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM/EDS) and X-ray diffraction (XRD) were employed to characterize the microstructure and phase composition of chloride-binding products on the concrete surface layer after 240 d of exposure.

Under S1, as S_W of RA increases, the content of unhydrated cement content in the new ITZ decreases, while porosity increases. In contrast, under S2, the opposite trend is observed. MIP results further indicate that under S1, total porosity, effective porosity, and the proportion of macropores (>100 nm) in the new mortar all increase significantly with increasing S_W, whereas under S2, these parameters decrease. Under S1, the water absorption capacity of RA decreases with increasing S_W; moreover, RA with high S_W may release water into the fresh mixture, resulting in an increase in the actual W_e/C ratio of the cement matrix. Conversely, under S2, higher S_W corresponds to a lower effective W_e/C ratio during the early hydration stage, thereby suppressing both hydration and pore development.

Since higher porosity and improved pore connectivity lead to increased chloride diffusivity, under S1, the values of D_RCM, D_e, and D_a all increase with increasing S_W. In contrast, under S2, the chloride diffusion coefficients decrease as S_W increases. SEM/EDS and XRD analyses confirm the occurrence of chloride binding in RAC, revealing the presence of chloride-bearing C-S-H gel and Friedel's salt after chloride ingress. During the early exposure stage (60-120 d), RAC under S1 exhibits a stronger chloride-binding capacity due to a higher initial degree of hydration. Under S2, however, a larger amount of residual unhydrated cement continues to hydrate during prolonged immersion, resulting in superior long-term chloride binding capacity. Moreover, under S1, the chloride binding capacity decreases with increasing S_W, whereas under S2, the opposite trend is observed. The decay rate of the chloride diffusion coefficient increases with S_W under S1 but decreases under S2. This may be attributed to the fact that, compared with S2, higher S_W under S1 leads to a larger early-age chloride diffusion coefficient, which promotes the formation of more corrosion products and significantly refines the pore structure, thereby accelerating the time-dependent reduction in chloride diffusivity.

Under S1, increasing S_W reduces the water absorption capacity of RA, and highly saturated RA may release water into the fresh mixture. This results in an increased W_e/C ratio in the new ITZ and mortar, leading to higher porosity and a greater proportion of macropores, and consequently higher chloride diffusion coefficients. In addition, the extra moisture promotes the hydration reaction, reducing the content of unhydrated cement particles in the new mortar and the new ITZ. In contrast, under S2, higher S_W corresponds to a lower effective W_e/C ratio during early hydration, which significantly suppresses hydration, pore development and chloride diffusion. SEM/EDS and XRD analyses confirm that chloride binding in RAC involves both physical adsorption and chemical binding. During the exposure period of 60-120 d, RAC under S1 exhibits a stronger chloride-binding capacity due to a higher initial degree of hydration. Under S2, however, the continued hydration of residual unhydrated cement during long-term immersion provides superior chloride binding capacity after 180 d. Furthermore, under S1, increasing S_W is detrimental to chloride binding capacity, whereas under S2, it is beneficial. The decay rate of the chloride diffusion coefficient increases with S_W under S1 but decreases under S2. This behavior is attributed to the larger initial diffusion coefficient under S1, which promotes the formation of more corrosion products and leads to progressive refinement of the pore structure over time.

In recent years, the continuous development of 3D reconstruction methods for porous media has promoted the integrated evolution of image modeling, performance prediction, and multiscale microstructure analysis. Although traditional reconstruction approaches achieve satisfactory matches for low-order statistical descriptors, they often struggle to accurately reproduce realistic geometric morphology and topological characteristics across multiple scales. Meanwhile, traditional generative adversarial network (GAN) methods exhibit remarkable advantages in handling complex porous structures (e.g., cement-based materials), yet are prone to issues such as mode collapse and gradient saturation. To address the aforementioned challenges, a high-fidelity 3D reconstruction method based on deep convolutional generative adversarial networks (DCGANs) is proposed in this paper, incorporating an autoencoder module within the generator to enhance multiscale feature representation. The model introduces a porosity constraint term in the adversarial learning framework and integrates Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), significantly improving training stability and convergence performance. Trained on CT images of new silicoaluminate cement paste at different curing ages, the model demonstrates good performance in both geometric morphology and statistical feature consistency of the reconstructed results.

The samples were prepared using the new silicoaluminate (limestone and calcined clay cement) system with 55% (mass fraction) Portland cement, 30% calcined clay, 15% limestone powder, and a water-to-binder ratio of 0.42. High-resolution micro-CT images were obtained at curing ages of 3, 28, 90 d, and 180 d. The images were preprocessed using Gaussian filtering and threshold segmentation to obtain binarized pore structures. A DCGAN architecture was developed by integrating 3D generative adversarial networks (GANs) with an autoencoder module. The framework incorporated gradient penalty to establish a WGAN-GP based on the Wasserstein distance, thereby constraining the Lipschitz continuity of the discriminator. A porosity constraint term was introduced into the adversarial learning framework to ensure global statistical consistency of the reconstructed microstructures. The reconstructed 3D volumes were validated against experimental microstructures using Minkowski functionals (porosity, surface area, Euler characteristic) and spatial correlation descriptors including the two-point correlation function (S2), lineal-path function, two-point cluster function, and surface correlation function.

The results demonstrate that the proposed model effectively reconstructs pore morphology and distribution characteristics across different curing ages, with the porosity of reconstructed volumes showing good consistency with measured samples. The introduction of a gradient penalty term in the loss function enhanced the training stability and convergence efficiency of the model. By incorporating porosity as a regularization term in the adversarial learning framework, the model was effectively guided to learn the porosity evolution trends across different curing ages, thereby improving the consistency between generated structures and experimental data. The generator employed a combined ReLU and GELU activation mechanism, which contributed to stable generator training. The integration of Instance Normalization in the discriminator further improved the quality of generated results and the generalization capability of the model. Comparative analyses based on Minkowski functionals and statistical descriptors revealed that the generated structures exhibit high agreement with the original CT images in terms of geometric morphology, connectivity, and spatial correlation. Compared to conventional reconstruction methods, the proposed approach improves computational efficiency by approximately 150% while maintaining structural fidelity.

This study constructed an efficient 3D generative adversarial network (GAN) model based on CT images of cement paste, achieving high-precision 3D reconstruction of pore structures at different curing ages. The proposed model demonstrates good stability and robustness during training and achieves high-quality reconstruction of pore structures in cement-based materials while effectively preserving structural diversity and morphological realism.

Insulating materials play a crucial role in high-voltage power transmission, protecting equipment and ensuring the reliability of the power grid by effectively isolating current and voltage. Conventional ceramic insulators (such as porcelain or aluminium oxide insulators) are widely used in high-voltage power transmission, but they have inherent limitations, including a short service life, high manufacturing costs and many surface defects, which are likely to cause electrical breakdown in the material. Furthermore, defects such as internal porosity in ceramics can lead to deterioration of their mechanical and electrical properties. In contrast, glass insulators, owing to their exceptional mechanical strength and excellent stability, have become one of the materials of choice for long-span transmission lines. Among these, sodium-calcium silicate (NCS) glass offers significant advantages, including high transparency, excellent physicochemical stability, ease of manufacture, and low cost, and thus holds great potential for application in the field of high-voltage power transmission and transformation equipment. However, the mechanical strength and electrical properties of NCS matrix glass are insufficient to meet the demands of modern power equipment for high electrical strength, high mechanical strength and low dielectric loss. Therefore, the glass composition was optimized, and the NCS matrix glass system was modified by doping with B₂O₃ and K₂O. The evolution of the network structure, optical transparency, mechanical strength and electrical properties with varying doping ratios was investigated to develop a material suitable for high-performance, long-life, high-voltage insulators.

The matrix glass composition was an NCS system. Two sets of glass samples were prepared using the high-temperature melting-quenching method. In Group B, B₂O₃ was added at varying molar fractions (0-20%(mole fraction)) while keeping the other components constant. In Group N, using the mixed alkali effect, K₂O was introduced to partially replace Na₂O, while maintaining the total content of Na₂O and K₂O constant. Subsequently, high-purity raw materials (SiO₂, CaCO₃, Na₂CO₃, H₃BO₃, K₂CO₃) were thoroughly mixed, placed in an alumina crucible and melted for 2 h at 1400 ℃. The melt was cast into preheated copper molds and annealed at 500 ℃ for 2 h to relieve internal stresses. The glass was cut into samples measuring 10 mm × 10 mm × 1 mm and 20 mm × 20 mm × 1 mm, and the surfaces were ground flat using a metallographic grinding and polishing machine. The material structure was characterized using differential scanning calorimetry (DSC), X-ray diffraction (XRD), and Raman spectroscopy. The optical properties were determined by UV-vis spectrophotometry. Mechanical strength was measured using a three-point bending test. The electrical properties included volume resistivity (measured at room temperature using a resistivity meter), dielectric loss, and dielectric breakdown strength (under DC voltage).

The addition of B₂O₃ significantly alters the glass network structure. When a small amount of B₂O₃ is introduced, its [BO₃] triangular units tend to replace some of the [SiO₄] tetrahedra of SiO₂, forming an irregular boron-oxygen network structure. This structural transformation reduces the proportion of bridging oxygen atoms in the glass network, leading to a decrease in network connectivity. As the B₂O₃ content increases further, [BO₄] tetrahedra gradually form, the number of bridging oxygen atoms increases, the glass network structure becomes denser, and the structural stability of the glass is enhanced. This structural change enhances the rigidity of the network. When 5%(mole fraction) B₂O₃ was added, the breakdown strength of the sample reached a maximum of approximately 31.93 kV/mm, with a flexural strength of 108.37 MPa. However, when the B₂O₃ content was excessive, a large number of [BO₃]/[BO₄] units appeared in the glass, leading to the breaking of the Si-O-Si chains and a decrease in network polymerization, which severely affected glass performance.

Compared to Na⁺ ions, the larger radius of K⁺ ions means that introducing K₂O increases steric hindrance within the glass network, resulting in a denser glass structure and thereby improving flexural strength. When 4% K₂O was added, the sample exhibited a flexural strength of 110.55 MPa, and the dielectric loss reached its lowest value (0.0027). The results indicate that an appropriate amount of K₂O doping for partial replacement of Na₂O can improve the glass's flexural strength and stiffness; however, the introduction of excessive K₂O may lead to an increase in defects within the glass network, thereby reducing the material's mechanical properties.

In summary, the incorporation of an appropriate amount of B₂O₃ into NCS glass, combined with the mixed alkali effect resulting from the substitution of Na₂O by K₂O, is an effective strategy for enhancing the mechanical strength and electrical properties of glass insulating materials. The results indicate that as the B₂O₃ content increases, the material's flexural strength exhibits a marked nonlinear change; when 5% B₂O₃ is added, both the flexural strength and electrical breakdown strength of the material are enhanced. The introduction of an appropriate amount of K₂O (partially replacing Na₂O) alters the polarizability of the glass via the mixed alkali effect, thereby enhancing the material's electrical breakdown strength while maintaining low dielectric loss. When the B₂O₃ and K₂O contents were 5% and 4%, respectively, the prepared sample exhibited high mechanical strength (flexural strength > 110 MPa), excellent insulation properties (room-temperature volume resistivity > 10¹² Ω·m, dielectric loss < 0.003 at 50 Hz) and high electrical strength (breakdown strength > 32 kV/mm). These findings provide a theoretical basis and material-design guidance for glass insulating materials used in high-performance electrical equipment.

As a key factor determining the long-term performance of concrete, is significantly affected by the plateau "triple-low" environment. This paper reviews the influence patterns and underlying mechanisms of the plateau "triple-low" environment from three aspects: environmental impact mechanisms, performance characterization methods, and regulation and enhancement measures. In terms of environmental impacts, studies have confirmed that individual environmental factors can cause retarded hydration, accelerated water evaporation, and unstable bubble structures, while multifactor environments exhibit more complex nonlinear characteristics, further reducing the workability of concrete. However, future research still needs to quantify the interactions among workability, hydration degree, and bubble structure under the coupled "triple-low" effects, and systematically elucidate their comprehensive mechanisms of action by incorporating factors such as temperature variations, radiation, and strong winds, to support the safe and efficient application of concrete in plateau regions. In terms of performance characterization, current studies mainly rely on combining environmental simulation with traditional rheological testing methods, yet there is a lack of specialized testing techniques for coupled environmental factors in plateau regions; although characterization models have been improved by incorporating environmental parameters, they still struggle to accurately predict rheological behavior under multifield coupling; although numerical simulations can analyze single-factor effects across multiple scales, they remain insufficient for simulating environmental coupling. For performance enhancement measures, the development and application of admixtures such as air-entraining agents, polymers, antifreeze agents, water reducers, and nanomaterials have been shown to be effective in improving concrete workability. Additionally, regulating bubble structures and optimizing mixing processes can compensate for air content loss and improve rheological properties. However, to achieve precise regulation of concrete workability in plateau environments, systematic methods for multifactor synergistic regulation need to be established, covering admixture formulation, process-parameter optimization, and bubble-structure system design.

AAC combined with FRP bars has broad application potential in marine engineering. Future research should focus on the following aspects: developing advanced microscale testing techniques to analyze the ITZ microstructure; investigating the bond durability of AAC prepared with seawater and sea sand under long-term field conditions; exploring the bond performance of ultra-high-performance AAC (UHPAAC) with FRP bars; and establishing more accurate prediction models by integrating multifactor test data with machine-learning methods.

This review systematically summarizes recent advances in enhancing the temperature resistance of alumina aerogels through three key strategies: structural-unit engineering, extrinsic doping, and isolation phase construction. These approaches have effectively delayed alumina phase transformation and suppressed high-temperature sintering, increasing the short-term service temperature from approximately 1000 ℃ to as high as 1600 ℃ while maintaining low thermal conductivity and structural integrity at elevated temperatures. However, for applications requiring higher temperatures and longer exposure times, future research should prioritize multiscale synergistic design, infrared opacification, mechanical reinforcement, and deeper investigation of the microscopic mechanisms of high-temperature phase transformation and sintering. Integrating the integration of emerging technologies such as multiscale simulation and artificial intelligence is expected to accelerate the development of alumina aerogels that simultaneously achieve ultrahigh-temperature structural stability and durable thermal insulation functionality.

Although significant progress has been achieved in understanding oyster-concrete interactions, the dualistic corrosion-protection mechanisms of oyster attachment remain complex and incompletely understood, with the evolutionary dynamics of oysters as dominant sessile organisms on concrete substrates and their lifecycle impacts on concrete integrity subject to ongoing debate owing to the intricate interplay of diverse marine environmental factors, seasonal variations, and developmental phases that lead to differential growth patterns and distinct effects on concrete durability. Future research should prioritize conducting systematic in situ field trials within authentic marine environments to enable precise mechanistic analysis of adhesion and corrosion processes, facilitate quantitative assessment of oyster life-cycle effects on concrete performance, and establish comprehensive models capable of predicting the transition between corrosive and protective outcomes based on critical thresholds of coverage density, biofilm maturity, and local hydrodynamic regimes. Furthermore, although oyster attachment can be detrimental under conditions of low-density colonization, the formation of stable, dense biofilms may confer significant protective benefits to concrete substrates by impeding aggressive aggressive-agent ingress, refining surface pore structure, and mitigating physical erosion, warranting in-depth investigations into this protective mechanism through integration of biological principles such as selective breeding and behavioral conditioning of target oyster species to promote rapid colonization of intertidal concrete surfaces and engineer cohesive fouling communities capable of providing effective long-term material preservation, ultimately contributing to the development of controllable, large-scale bioprotective strategies for sustainable marine infrastructure management.