During unconventional energy extraction, substantial volumes of fluid are injected into low-permeability reservoirs to facilitate hydraulic fracturing, creating an extensive network of fractures that enhance fluid mobility. However, such large-scale fluid injection can lead to the initiation and propagation of fractures, potentially triggering detectable seismic events that pose risks to human life and infrastructure. To better understand these processes, in situ dynamic scanning imaging of hydraulic fracture propagation and water-rock interactions in tight sandstones has been conducted using X-ray computed tomography (CT). Our experimental findings reveal that fluid infiltration weakens rock strength, thereby promoting rock failure. Under the influence of fluid injection, microfractures undergo a continuous cycle of generation, expansion, and coalescence, ultimately forming interconnected hydrological pathways. These pathways are critical for the sustained propagation of fractures within the rock. CT imaging highlights a positive feedback loop between fracture growth and the enhancement of fluid diffusion. Notably, the rock at the dry-wet interface of the fluid front is particularly susceptible to fracturing. Additionally, the rates of fracturing vary among different fractures and tend to progressively decrease as the fractures extend deeper into the rock.

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.

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.

Water-rich sand layers are frequently encountered as adverse geological conditions during underground construction. Polymer slurry grouting has been widely recognized as an effective technique for reducing permeability and enhancing the stability of such strata. In this study, a mathematical model is established to describe the diffusion behavior of polymer slurry in porous media under dynamic water conditions and is further validated through laboratory experiments. The theoretical formulation of the slurry permeation process is developed based on Darcy's law, the Hagen-Poiseuille flow principle, and the physicochemical characteristics of the slurry. The derivation primarily focuses on analyzing the dynamic response of the slurry under the influence of water flow, considering the effects of flow velocity, grouting pressure, and sand-layer porosity on diffusion behavior. To verify the proposed model, a visualized grouting simulation system was designed to observe the diffusion process of polymer slurry in water-rich sand layers. The results demonstrate that slurry diffusion is significantly affected by grouting pressure, porosity, and water flow velocity. The observed staged diffusion characteristics, dynamic evolution patterns, and directional effects are in good agreement with theoretical predictions. Furthermore, the average relative deviations between the theoretical and experimental results for diffusion pressure and diffusion distance are both less than 25 %, confirming the reliability of the proposed model. Additionally, this study identifies distinct differences in slurry diffusion between porous and void media. In porous media, slurry propagation encounters greater hydraulic resistance, leading to rapid pressure attenuation and a limited diffusion range. Conversely, diffusion in void media occurs more smoothly due to the continuous cavity structure, resulting in slower pressure decay and a substantially larger diffusion radius. These findings elucidate the mechanisms governing slurry diffusion under dynamic water conditions and provide a theoretical basis for optimizing grouting parameters and improving construction efficiency in water-bearing strata.

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.

Rock mass discontinuities arise from tectonic movements and other geological processes, reflecting the evolution of the Earth's crust. These discontinuities significantly influence the physical properties, deformation characteristics, and energy release mechanisms of the crust. Therefore, recognizing discontinuities is crucial for understanding the evolution of geological structures, analyzing the physical and mechanical properties of geological bodies, and investigating geological hazards. Traditionally, discontinuity recognition has relied on manual interpretation or automated algorithms based on pixel brightness. However, these methods often struggle to strike a balance between efficiency and robustness. To overcome these limitations, we leveraged deep learning techniques that integrate the strengths of both approaches, enabling the recognition of automated discontinuity with expert-level accuracy. To accomplish this objective, we developed and open-sourced the first large-scale deep learning database for rock mass discontinuities, featuring over 300,000 annotated discontinuities. The YOLOv8x-seg model was extensively trained on this database and evaluated across diverse and complex scenarios. The results demonstrated the model's capability to accurately recognize discontinuities even under challenging conditions. Furthermore, we expanded the test set to include rock masses from various global locations, as well as underground rock masses, soils, and artificial structures, where the model consistently achieved effective recognition. The model consistently delivered accurate results, highlighting its strong generalization capability. A comparative analysis revealed that its performance closely aligns with expert manual interpretations. Our open-source database enables researchers to train various deep learning models and achieve equally high-performance results.

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.

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.

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.