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.

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.

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.

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.

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.

In hard rock tunnel excavation, controlling the blasting profile to prevent overbreak and underbreak is critical for safety and cost-effectiveness. Discontinuities such as joints and faults significantly affect the mechanical properties of the rock mass, and their distribution critically influences the blasting outcomes. This study explores the impact of joint distribution on the tunnel blasting profile through field measurements and numerical simulations. Real-time monitoring of the tunnel face was conducted using the digital twin method, capturing both rock discontinuities and blasting profiles. Field results revealed that overbreak tends to occur at joints outside contour boreholes, where the joints lead the blasting profile diverging from the borehole connection line. To quantify this effect, dynamic finite element simulations were conducted to assess the influence of borehole-joint distances (d = 25 cm, 50 cm, and 100 cm) and intersecting joint angles (α = 60°, 90°, and 120°) on blasting stress wave propagation and rock fracture development. The results demonstrated that joints within the hard rock mass guide and restrain the propagation of blasting stress wave, leading to the formation of a fracture zone induced by the reflected stress wave (the RSW fracture zone). The morphology of the RSW fracture zone closely matched the field blasting profile, validating the numerical simulation results. Furthermore, the borehole-joint distance and the intersecting joint angle were found to govern the extent and geometry of the RSW fracture zone. These findings provide valuable insights for optimizing blasting designs in jointed hard rock masses to control tunnel excavation profiles better.

Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks. This study focuses on rock slope failures and proposes a framework for online identification of accelerating precursors and dynamic probabilistic prediction of failure time grounded in Bayesian inference. By integrating the Bayesian online change-point detection (BOCD) method with a typical dimensionless trend (TDT) model, the BOCD-TDT algorithm is first developed for online identification of acceleration events and their corresponding onset of acceleration (OA). Subsequently, a Bayesian approach is employed to estimate the parameters of the inverse velocity (INV) method, enabling the dynamic probabilistic prediction of slope failure time while quantifying observational and model uncertainties across different accelerating deformation stages. Building on this, the influence of starting point (SP) selection, trend update (TU), and multi-data fusion on prediction reliability is evaluated, and a novel decision criterion for impending slope failure is proposed. The feasibility of the proposed methods is then validated using 73 rock slope failure cases. Results show that using INV data, the BOCD-TDT algorithm can reliably identify acceleration events and the corresponding OA. In time-to-failure analysis, the reliability of dynamic failure predictions can be enhanced by incorporating both observational and model uncertainties corresponding to the deformation stages into the Bayesian prediction model, along with TU detection and multi-data fusion. The proposed failure probability criterion provides valuable guidance for the identification of impending failure and the establishment of ultimate alert thresholds.

In this study, the influences of the thermoelastic effect and fluid viscosity-temperature effect (VTE) on hydraulic fracture growth in deep reservoirs were investigated. A computational model that integrates the thermoporoelastic effect and VTE was developed on the basis of the displacement discontinuity method (DDM). The temperature distribution within fractures is determined using a first-order upwind scheme. Using this simulator, this study systematically evaluated the impacts of the poroelastic stress, thermoelastic stress, and VTE of the fracturing fluid on fracture propagation. Furthermore, the dominant controlling factors were identified in both the viscosity- and toughness-dominated regimes. The results show that (1) the thermoelastic stress exhibits behavior opposite to that of poroelastic stress, reducing the injection pressure and increasing the fracture width. (2) Under viscosity-dominated conditions, the influence of the VTE is more remarkable, whereas the thermoelastic effect on fracture propagation is relatively weak. Under toughness-dominated conditions, the influence of the thermoelastic effect on fracture propagation remains relatively weak, and the VTE can essentially be disregarded. (3) When proppant transport is considered, for small proppant particles, the transport distance increases from 88 m to 100 m when the VTE is considered because the VTE increases the fracture length. For large proppant particles, owing to the decrease in viscosity with increasing temperature, the proppant transport distance is significantly reduced from 86 m to 70 m. These results indicate that reasonably selecting the proppant size and paying more attention to the VTE of the fracturing fluid in deep reservoir fracturing are crucial.

This study investigates the effectiveness of combined tip-and-side post-grouting on large-diameter bored piles in deep fine sand layers. Field tests were conducted on nine piles for the Shishou Yangtze River Highway Bridge project. A detailed comparison of pile performance pre- and post-grouting assessed the technique's influence on ultimate bearing capacity and side resistance. The distribution and effectiveness of the cement grout were analysed using core drilling and the standard penetration test (SPT). An equation correlating post-grouting side resistance with the pre-grouting SPT index (N_SPT) was established. Results demonstrate a substantial improvement in pile bearing capacity after grouting. Ultimate bearing capacity increased by 76 %-152 % after grouting. Longer piles on the main bridge exhibited more pronounced enhancement, achieving ultimate capacities 145 %-206 % higher than those of the shorter approach bridge piles. This is attributed to the greater total cement volume applied along their sides. Critically, combined grouting outperformed side-only grouting, enhancing both side and tip resistance. Core drilling confirmed the spread of cement grout around the piles, confirming the method's effectiveness. SPT results indicated significant increases in the soil N_SPT adjacent to the piles following grouting. These findings provide directly applicable data for designing the bridge pile foundations and offer essential guidance for comparable projects in deep fine sand layers.

Tunnel portal sections have historically been more susceptible to earthquakes than other components, exhibiting significant seismic damage. However, critical seismic behaviors of portal sections remain unrevealed owing to insufficient consideration of actual topography. Moreover, the extent of asymmetric seismic responses induced by topography remains unclear, which is essential for seismic design. To overcome these limitations, this study replicated the actual geological conditions of a tunnel portal section, including the portal slope, topography, slope and tunnel supports, and the often-overlooked portal wall using large-scale shaking table tests. The asymmetric seismic responses and their impact ranges identified in the experiments were validated through numerical simulations. The results revealed that the seismic damage to the slope is attributable to the presence of the tunnel, with slope acceleration near the tunnel portal increasing by 20 %-40 % compared to slopes without a tunnel. Additionally, the tunnel facilitates seismic wave propagation in specific directions, leading to further seismic damage across the portal section. Portal walls, being exposed structures, are susceptible to higher seismic strain and acceleration than tunnel linings and thus warrant increased attention. Importantly, the asymmetric seismic response was found to vary based on different sides and influence ranges. Within 15 m of the portal, the tunnel was dominated by the open-side asymmetric response of acceleration, strain, and displacement. From 15 to 35 m range, the seismic response of the mountainside was more pronounced, exhibiting increased seismic earth pressure and stress on the right sidewall. Seismic earth pressure and stress diminished within the 35-75 m range and steadily decreased beyond 75 m. This enhanced understanding of seismic behaviors facilitates the targeted establishment of future seismic fortifications based on these classified ranges.