Stratigraphic interface characterization and strength parameter assessment of geomaterials constitute fundamental research priorities in geological and geotechnical engineering. While measurement while drilling (MWD) and drilling process monitoring (DPM) have emerged as critical techniques for acquiring real-time drilling parameters, inherent limitations in data interpretation persist. The critical challenge of random fluctuations in MWD-derived penetration rate measurements exhibits poor correlation with the stratified homogeneity characteristics of geological formations. Such discrepancies undermine the reliability of stratigraphic classification and mechanical property analysis. Through systematic comparison of MWD and DPM datasets combined with quantitative parameter evaluation, this investigation reveals significant methodological distinctions in data acquisition accuracy. Machine learning-enhanced analysis employing Support Vector Machine (SVM) algorithms demonstrates that DPM-derived parameters provide superior stratigraphic identification capabilities. Our findings indicate that DPM implementations achieve 20.57 % and 38.01 % higher resolution in interface detection along two drill-holes compared to the conventional MWD approaches. This improvement allows for better prediction of stratigraphic profiles and more precise guidance in subsequent geological and geotechnical engineering practices.

Excavation-induced retaining wall deflection (RWD) significantly influences the safety of surrounding built environment. To predict the three-dimensional RWD in heterogeneous strata, a new partial differential equation (PDE) is derived in this study, and two prediction models are proposed, i.e. the physics-informed neural network (PINN) model and the data-driven PINN model. As a physical constraint, the new PDE is crucial to the loss functions of these models. Then, the validity of the models is verified and analysed using a subway deep-foundation pit. The results show that the training times of both models are controlled within 900 s, which is a significant reduction compared to that of the conventional numerical model. In addition, the prediction accuracy of the data-driven PINN model is higher than that of the numerical model, while that of the PINN model is slightly lower than that of the numerical simulation. However, in contrast to the data-driven PINN model, the PINN model can identify irregular soil interfaces in heterogeneous strata to learn the deflection continuity conditions at irregular interfaces and realize RWD prediction in non-uniform distributed strata. In practical applications in foundation pit engineering, the selection of the PINN and data-driven PINN models can be conducted according to the in situ distribution conditions of the strata to enable the early prediction of potential RWD, thereby providing a reliable basis for the further optimisation of retaining structures design.

Effective sealing of geological fractures is essential for subsurface stability and mitigating environmental risks such as groundwater contamination and inefficient CO₂ sequestration. Enzymatically Induced Carbonate Precipitation (EICP) offers a promising bio-mediated approach due to its ability to fill and seal fractures. However, real-time precipitation patterns and clogging behavior of EICP under varying fracture and flow conditions remain poorly understood. This study employs a transparent fracture model with visualization to systematically investigate the effects of fracture aperture, flow conditions, and surface roughness on EICP-mediated sealing. Results indicate that fractures with narrower apertures promote tortuous finger-like flow paths, while wider-aperture fractures show more uniform deposition, with fewer but wider preferential flow paths. An appropriate injection rate around 1 mL/min ensures uniform precipitation and effective clogging, avoiding inlet clogging at lower rates (0.1 mL/min) and flushing effect reducing deposition at higher rates (10 mL/min). Additionally, rough fractures exhibit higher precipitation efficiency and greater permeability reduction, driven by their irregular surface geometry, which creates more deposition sites and complex flow compared to smooth fractures. Image processing reveals that precipitation patterns in rough fractures match closely with aperture distribution, compared to more concentrated deposition in smooth fractures. These findings provide insights for optimizing EICP-mediated fracture sealing, with implications for groundwater protection and geotechnical practices.

To realize the soil reinforced through the carbonation of ternary binder under ambient pressure and mild conditions, the present study introduces triethanolamine (TEA), which serves as an effective carbonation accelerator. Through the unconfined compressive strength (UCS) test, the soft soil solidified with ternary eco-binder consisting of ground granulated blast-furnace slag (GGBS), metakaolin (MK), and calcium carbide residue (CCR), subjected to carbonation, is investigated. The effect of TEA on the carbonation of soil is evaluated by the UCS and the CO₂ mineralization. This study clarifies the influence factors, including the initial water content, TEA dosage, binder constituent ratio, and content. The optimal binder constituent ratio for the strength growth and carbonation efficiency of carbonated soil is approximately 4:4:2 for GGBS, CCR, and MK, respectively. The incorporation of TEA at a low dosage (<0.15 %) enhances the strength of carbonated soil, whereas the high dosages impair the strength. The synergistic effect of TEA and carbonation further improves the strength and compressibility of soil. The soil with 1.5 % TEA carbonated for 7d exhibits a 44.8 % increase in strength compared to that without TEA, which is attributed to a 2.2-fold increase in carbonation efficiency. The addition of TEA accelerates the ion dissolution and CO₂ dispersion, promoting the carbonation reaction in soft soil. Calcite and aragonite precipitate during carbonation, contributing to the strength development of soil. The carbonates phase difference and the pore structure density with different TEA dosages are also demonstrated to be the strength influence factors.

Landfill facilities around the world are designed to protect the environment and public health by using impermeable liner systems that isolate the waste and leachate produced from the waste. However, the functionality of liners has been reported to be significantly compromised by environmental loading due to the seasonal climatic and physico-chemical changes that alter their volume deformation and hydraulic characteristics. Bentonite admixed natural soils are employed as liner materials if they meet the hydraulic conductivity requirement in their as-compacted state. However, limited studies addressed the effects of wet-dry cycles combined with chemical contamination on the volumetric and hydraulic behaviour of bentonite admixed natural soils. In this study, Indian red soil was ameliorated with 10%, 20%, and 30% bentonite by weight, and the mixtures were subjected to alternate wetting and drying cycles using distilled water, 0.4 M NaCl, and 0.4 M CaCl₂ solutions. All red soil-bentonite specimens met the hydraulic conductivity design criterion of 1 × 10^-7 cm/s in their as-compacted states. However, significant variation in hydraulic behaviour was observed at the end of the wet-dry cycles, particularly with chemical contamination. The microstructural examination through scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) revealed an increase in macropores volume with wet-dry cycles and increase in the induced osmotic suction, which was found to be a key factor influencing the hydraulic conductivity.

Supersulfated cement (SSC) is considered an environmentally friendly alternative to ordinary Portland cement (OPC), while its stabilization efficiency on dredged sediment (DS) is still unclear. Three types of SSC were prepared by combining ground granulated blast-furnace slag, alkali-activator NaOH, and a sulfate waste source, yielding SSCE (from electrolytic manganese residue), SSCP (from phosphogypsum), and SSCD (from desulfurization gypsum). To further enhance the stabilization efficiency of SSC on DS, nano-SiO₂ (NS) and nano-Al₂O₃ (NA) were incorporated individually and as a composite blend. Mechanical properties and microstructural analyses were conducted to evaluate the stabilization efficiency and elucidate the underlying mechanisms. The leaching toxicity of SSCE-stabilized DS was investigated via leaching tests. The results showed that both alkali-activation and nano-modification can significantly improve the strength development of SSC-stabilized DS. At least 15 % NaOH was required for SSC to achieve the same stabilization efficiency as OPC. The optimum NA-modified SSCD-stabilized DS demonstrated superior strength compared to OPC-stabilized DS. Composite NS/NA-modification was more efficient than using NS or NA individually. For DS stabilized with SSCE, SSCP, and SSCD, the optimal NS-to-NA mass ratios were 7:3, 3:7, and 3:7, respectively. Notably, the nano-modified SSCE-stabilized DS showed no environmental risks. Incorporating NS and NA into SSC-stabilized DS respectively promoted the formation of C-S-H gel and ettringite. A micro-mechanism model was developed to explain the strength evolution of nano-modified SSC-stabilized DS. This study provides a theoretical basis for the application of SSC in DS stabilization, and facilitates the collaborative resource utilization of industrial solid wastes and DS.

Incineration bottom ash (IBA) holds attractive potential as a construction material, yet its shear behavior under cyclic loading remains insufficiently understood. This study comprehensively characterizes the monotonic and cyclic simple shear behavior of Singapore-derived IBA under constant volume conditions, with particular emphasis on its reuse potential in dynamic load-bearing applications. Key findings reveal that: (1) The material exhibits marked strain-hardening characteristics, demonstrating a density-dependent friction angle increment from 38.3° (loose state) to 42.5° (dense state). (2) Mechanical performance shows strong dependence on Si-Ca-Fe/Al ternary chemical composition and particle gradation characteristics. (3) Distinct failure modes emerge under different loading conditions - liquefaction dominates under unidirectional cyclic simple shear (UDCSS) conditions at low cyclic stress ratios (CSRs) and confining pressures, while bidirectional cyclic simple shear (BDCSS) loading induces cyclic mobility failure at elevated CSR levels, with corresponding cyclic resistance ratios (CRRs) showing a 30 % reduction in BDCSS compared to UDCSS configurations. (4) Pore pressure ratio (R_u) evolution follows a triphasic pattern: liquefaction failures exhibit rapid R_u acceleration in initial and tertiary phases (terminal R_u > 0.9), contrasting with cyclic mobility failures characterized by decaying R_u growth rates and lower terminal R_u values. (5) Notably, the established correlation between CRR and normalized shear wave velocity (V_s1) aligns closely with that of sand-gravel mixture with 5 % fines, which demonstrates the comparable cyclic load-bearing capacity of IBA to that of conventional construction materials. The study highlights the effect of load direction, particle size, and mineralogy in design applications and supports IBA's suitability for reuse in infrastructure subjected to dynamic loads.

Accurate extraction of rock mass discontinuity parameters is crucial for stability assessment and engineering safety. High-resolution remote sensing facilitates automated extraction, but its effectiveness relies heavily on precise normal estimation to ensure geometric reliability. Conventional methods struggle to preserve sharp features such as edges and corners, thereby reducing accuracy. To address this, we propose a normal estimation method based on local geometric adjustment that enhances feature extraction while maintaining sharp geometries. The approach consists of four steps: (1) classifying points, (2) applying normal and axial projections, (3) fitting segmentation lines via least squares, and (4) refining normals by optimizing local neighborhoods. The proposed method was evaluated on computer-aided design (CAD) models, real objects, and rock mass point clouds, and benchmarked against eight representative algorithms, including principal component analysis (PCA), 2-Jet PCA, Voronoi-based PCA, PCPNet, neural gradient function (NeuralGF), low rank representation (LRR), normal estimation via shifted neighborhood (NSN) and pair consistency voting (PCV). Experimental results demonstrate that our method achieves superior accuracy and efficiency, significantly improving structural plane extraction and ensuring better preservation of sharp geometric features.

Traditional deterministic numerical simulation often has a poor prediction performance for landslide-induced wave run-up (LIWR) hazards, as it neglects the effects of uncertainty. The limitation for efficiently quantifying the uncertainties in primary parameters remains largely unsolved. In this study, we propose a probabilistic evaluation method, integrating the adaptive Kriging (AK) metamodel method and probability density evolution method (PDEM) based on generalized F-discrepancy. A Taylor expansion-based adaptive design strategy is applied to construct the global AK model over representative points generated by generalized F-discrepancy, thereby approximating the numerical physical response (i.e., maximum LIWR). Using these approximate responses, the PDEM is used to compute the exceedance probabilities that LIWR heights exceed elements at risk based on a construction of virtual time, and then a probabilistic criterion is introduced to classify hazard zones. The proposed method is demonstrated via two examples: Example Ⅰ, which possesses risk element (building), and Example Ⅱwith water-level variations. The results indicate that the proposed method has an acceptable performance (showing a 1.7 % difference in exceedance probability compared to Monte Carlo simulation with 50,000 samples) with low computation cost (requiring 284 deterministic analyses). For two specific scenarios in this study, the wave induced by the landslide exhibits a solitary-like leading wave. The proposed probabilistic method provides promising prospects for quantifying LIWR uncertainties, and is helpful for direct, efficient, and low-cost quantification assessment of cascading hazards.

In the natural environment, the soil structure can be weakened by temperature fluctuations and climatic changes. Nevertheless, the dynamic behavior of expansive soils, especially those with high swelling and pronounced fissure properties, subjected to wetting-drying-freeze-thaw (WDFT) cycles has been rarely investigated. Undisturbed and remolded samples, made of Xinjiang's highly expansive soils, were evaluated in this study through comprehensive resonant column tests conducted at several confining pressures and WDFT cycles. A typical hyperbolic model demonstrated the decay law of shear modulus with strain. An estimated model of the maximum shear modulus, incorporating the two factors, was developed, and it was found to be in good agreement with the measurement results. The results reveal that strain, WDFT cycle, and confining pressure have qualitatively uniform effects on the shear modulus of natural soils containing fissures and recompacted samples. However, the maximum shear modulus of the undisturbed samples is lower by 0.83-13.24 MPa due to the presence of initial fissures, except for the confining pressure of 400 kPa. Also, their responses to confining pressure are more significant, with the shear modulus increased by up to 20 %-124 % relative to that at 25 kPa. Furthermore, the relative difference in the shear modulus (up to about 60 %) between the two samples tested under low confining pressure conditions deserves special attentions. The quantitative differences in shear modulus and cumulative damage effect of the tested samples are attributed to the initial fabric and microstructural evolution, as observed by Scanning Electron Microscope (SEM). This research enriches the theoretical framework for analyzing the ability of soils to resist shear deformation under small strain, which is instructive for disaster prevention and mitigation in expansive soil regions, considering the effects of climate change.