This study investigates particle crushing mechanisms in granular soils during shearing through staged triaxial compression experiments performed at prescribed axial strains and varying confining stresses, integrating a high-performance acoustic emission (AE) measurement system. The study analyzed particle crushing-related parameters using grain size distribution (GSD)-based indices (relative breakage index B_r and its rate ΔB_r) and AE-based parameters (high-frequency AE hits and hit rates ). The results confirm the feasibility of high-frequency AEs (>100 kHz) in comprehensive quantification of particle crushing, with a strong linear relationship observed between and B_r. Significant particle crushing occurs within the initial 5 % of axial strain, which correlates with the yielding and peak-stress phases. This process yields fragments with a size range of 0.425-2 mm. Increased confining stresses result in a steady rise in B_r and , suggesting that large strains are required for stable particle grading. The evolution trends of different high-frequency AE ranges reveal a shift to complex crushing mechanisms, such as particle abrasion/grinding and corner breakage/particle splitting, highlighting the role of stress and strain levels in influencing particle damage behavior.

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.

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.

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.

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.

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.

The mixed rock-ice deposits in high-latitude areas usually come from the accumulation of rock-ice avalanches. Melting tests and temperature-controlled triaxial tests were conducted on rock-ice mixture samples with varying volumetric ice contents (0.25, 0.5, and 0.75), environment temperatures (0.5 ℃, 1 ℃, and 1.5 ℃), and temperature control durations (20 min, 40 min, and 60 min), aiming to investigate the thermodynamic behavior of rock-ice mixed deposits under an ablation environment. Our findings reveal that the melting behavior of rock-ice mixtures mainly occurs in the surface layer; then, ice meltwater transports along the surface seepage path to the bottom of the sample. Notably, the basal meltwater gathering zone leads to accelerated melting of the ice debris, and the cohesion and occlusion between the rock debris in this zone almost disappear, ultimately resulting in severe basal damage. Triaxial test results indicate that the basal damaged zone leads to an easy initial compression process of the rock-ice mixture samples and forms a basal strain effect. Statistics show that ice content, environment temperature, and temperature control duration all show a negative impact on the mixture's peak strength in thaw environments, which also exacerbates the basal strain effect, with a maximum basal strain of 8.61% and a corresponding mass loss ratio of 28.69%. Finally, the mechanisms of the secondary sliding of the mixed deposits and the failure mode of the rock-ice deposit dams induced by ice debris melting were discussed.

This study presents a novel framework for evaluating slope stability in spatially variable soils by integrating a newly developed sequential limit analysis based on the Hellinger-Reissner functional, utilizing the node-based smoothed finite element method (NS-FEM), with a newly proposed deep learning (DL) approach termed multi-downsampling hybrid Linformer-convolutional neural networks (CNNs). The NS-FEM-based mixed formulation of limit analysis (MFLA) enhances computational accuracy and convergence by smoothing strain fields and mitigating numerical discontinuities commonly encountered in standard finite element methods (FEMs). This method generates reliable datasets for stochastic simulations of slope stability under both static and seismic loading conditions. To address the computational expense of specific simulations, we propose the multi-downsampling hybrid Linformer-CNN model, a sophisticated DL architecture that employs dual parallel pathways with distinct downsampling strategies - AveragePpooling1D for medium-scale feature extraction and MaxPooling1D for coarse-scale feature extraction. Each pathway integrates one-dimensional (1D) CNNs for local feature extraction and Linformer-based self-attention mechanisms to efficiently capture global dependencies. The parallel downsampling strategies balance computational efficiency with feature granularity, enabling the model to leverage both local and global data characteristics effectively. The extracted multi-scale features are concatenated and further processed through fully connected networks (FCNs) to accurately predict the factor of safety (FoS) of slopes. Comparative analyses demonstrate that the hybrid Linformer-CNN model outperforms traditional FCN and CNN architectures, achieving robust and precise predictions with a mean absolute percentage error (MAPE) below 10 %. Additionally, the proposed framework significantly reduces computational time, highlighting the potential of integrating NS-FEM-based MFLA with advanced DL architectures for rapid and reliable slope stability assessment in geotechnical engineering.

Current in-situ stress determination methods are typically conducted inside a drillhole after its creation. However, the drilling process itself is not utilized for measuring in-situ stress or rock strength, despite being a form of direct mechanical testing on the rock mass. Crucially, drilling contains valuable information about in-situ stress and rock strength, as rocks under high compressive stresses exhibit greater strength. This paper presents a novel in-situ stress determination method, supported by the experimental result of rock drilling monitoring tests using a mine hydraulic-rotary drilling machine. Key drilling parameters-including thrust force, rotation speed, torque and drilling speed-are monitored in real time to determine the drilling specific energy per unit volume of rock. A concave-upward relationship between drilling specific energy and rotation speed is identified, which can characterize rock compressive strength and tensile strength with consistent regularity. Further drilling tests are conducted on the same rock samples under varying confining pressures. Results indicate that as confining pressure increases, the concave-upward curve of drilling specific energy shift upward, reflecting enhanced rock strengths due to confinement. The paper outlines the complete methodology for in-situ stress determination using drilling monitoring techniques, bridging the research gaps among drilling monitoring, rock mechanics, and in-situ stress analysis.