Hazardous geophysical granular flows, such as debris flows and rock avalanches, can exert intense impact forces on obstacles and threaten downstream structures located in their paths. Installing protective structures can mitigate damage, but quantifying their influence on flow evolution and impact loading remains challenging. This study investigates the interactions of granular shock waves (GSWs) generated in front of two cylindrical obstacles with varying spacings through chute experiments and discrete element modeling. Impact pressure sensors were mounted on the upstream surface of each cylinder and on the chute bed to measure dynamic impact pressures in the GSW region. Granular flow velocity and depth were obtained using image processing. Results demonstrate that cylinder spacing significantly influences the geometric characteristics of GSWs. Runup increases with steady-state Froude number (Fr_steady) but decreases as spacing narrows. The granular vacuum length grows with bed slope but decreases significantly with decreasing cylinder spacing. Impact pressures on the cylinders and the chute bed increase linearly with Fr_steady. Low-frequency power spectral density (PSD) is positively correlated with Fr_steady, whereas centroid frequency and pressure impulse counts exhibit low sensitivity to Fr_steady. The dimensionless impact pressure coefficient (α) decreases nonlinearly with increasing Froude number (Fr). At low Fr, α values for dry granular flows are lower than those for debris flows, but the difference diminishes at higher Fr. These findings may improve our understanding of granular flow-obstacle interactions and might help to design protective structures.

During geotechnical construction, flawed rock masses experience dynamic cyclic disturbances, leading to cumulative deformation and progressive damage. Consequently, elucidating the fracture mechanisms under cyclic loading is crucial for ensuring the safety and prolonged operation of deep underground engineering. This study investigated the mechanical responses of the surrounding rock at different locations by conducting triaxial tests on flawed granite using three distinct cyclic loading and unloading paths. Based on the maximum tangential stress criterion, a fracture mechanics model for open flaws was developed to analyze the intrinsic influence of confining pressure and flaw inclination on crack initiation behavior. The results indicate that graded unloading of confining pressure significantly weakens the flawed rock mass, reducing its peak stress to only 77.5 % of that observed under constant confining pressure. Conversely, flawed rock masses exhibit a substantial increase in bearing capacity under increasing graded cyclic loading, achieving a peak stress 19.3 % higher than that under cyclic disturbance loading. At a constant confining pressure of 40 MPa, the type of disturbance loading has no significant effect on the failure mode. The flawed granite specimens form a nearly V-shaped shear failure zone along the open flaw. However, confining pressure unloading induced a more complex shear-tensile composite failure mode in the specimens. The crack initiation angle increases nonlinearly with confining pressure, but decreases gradually as the flaw inclination angle (β) increases. These findings provide valuable insights for the safe construction of deep underground engineering.

Borehole instability in heterogeneous rocks poses a significant challenge in geo-energy engineering. The deformation and failure around boreholes are heavily mediated by the inherent heterogeneity of rocks. Here, we examined borehole breakout under hydrostatic pressure through both laboratory tests and numerical simulations on sandstone samples. Laboratory experiments demonstrated symmetrical V-shaped failures across various borehole diameters. To replicate these observations, we developed a heterogenous UDEC Voronoi model where the material heterogeneity was interpreted by assigning Weibull-distributed inter-grain contact parameters. The rigorous-calibrated numerical modeling can effectively capture the microscopic damage process and match the observed macroscopic failure modes. Simulations showed that reducing the borehole diameter increases the critical hydrostatic pressure required for borehole failure and prompts a shift from tensile to shear-dominated failure behavior. While stress anisotropy primarily governs the overall breakout morphology, rock heterogeneity influences the specific locations of crack initiation, leading to localized stress concentrations that shape the ultimate failure patterns. These results provide valuable insights into borehole stability in heterogeneous rocks and guide engineering design and pertinent risk assessment.

Evaluation of compressive strength in underground lining structures is critical for ensuring structural integrity and safety. Traditional assessment methods are often destructive, time-consuming, and impractical in confined environments such as tunnels and utility corridors. This study introduces an automated, nondestructive approach to visualize and estimate the compressive strength of underground concrete lining using hyperspectral imaging (HSI) combined with deep neural network (DNN) models. High-dimensional spectral data of concrete lining are assembled and trained to develop two DNN-based regression models, namely the Mono-Spectrum Deep Neural Regressor (MS-DNR) and the Segmented-Spectrum Deep Neural Regressor (SegS_DNR). Utilizing the SegS_DNR model, two-dimensional (2D) compressive strength distribution heatmaps were generated for visualization and assessment of strength variations. The SegS_DNR model demonstrated excellent predictive performance, achieving a coefficient of determination () of 0.925 and a Residual Prediction Deviation (RPD) of 5.28 on the testing set for compressive strength estimation. The idea is further validated in site by investigating the capability of identifying the defect regions of the tunnel concrete lining, namely the cracked, spalling, and leaking areas, and demonstrated promising performance in comparison with experienced inspectors on site. This approach offers a contact-free technique for automated structural health monitoring, contributing to safer and more sustainable underground maintenance practices.

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.

The strain energy storage index (W_ET) is a crucial index for evaluating rockburst proneness. Interestingly, when conducting tests to obtain W_ET, variations exist in the shape of coal or rock specimens. However, whether shape factors affect W_ET has not been theoretically and experimentally verified. In this study, to investigate the independence of W_ET from specimen shape effects, its rationality was first theoretically derived based on the linear energy storage (LES) laws of rock, indicating that W_ET is influenced by the energy storage coefficient (ESC) of the rock. Two typical rock materials (granite and red sandstone) with different rockburst proneness were selected to verify the migration effect of cubic and cylindrical specimens on W_ET via uniaxial compression tests. The experimental results revealed that the mechanical behavior characteristics of rocks were affected by the shape of cylindrical and cubic specimens, whereas the W_ET and ESC were opposite. Furthermore, the practical W_ET values closely approximate the theoretical values of energy storage-dissipated ratio predicted by the LES law, converging to the peak-strength strain energy storage index (). Based on the LES law, the influence of specimen shape on W_ET and was further discussed, concluding that W_ET and are independent of specimen shape effects. Furthermore, the is more stable than W_ET and reflects the relative magnitude of energy storage and dissipation during the entire pre-peak of rock. Thus, the peak-strength strain energy storage index can be used as a substitute for W_ET in evaluating the rockburst proneness of rock.

Taking the Banbiyan dangerous rock mass as the focus, this study employs field investigations, model experiments, and numerical simulations to explore the instability mechanisms of dangerous rock masses on bank slopes containing a single shear band under the deterioration of reservoir water. The results indicate that the failure mode of the dangerous rock mass is collapse of rock mass in the hydrofluctuation belt (HFB) - internal damage to the dangerous rock mass - development and through-going of fractures on both sides - sliding failure of the lower rock detaching from the parent rock. As the shear band gradually deteriorates, stress concentration develops around it near the highest water level. Within the rock mass close to the highest water level, a phenomenon of unloading occurs, and the pore water pressure at the shear band-bedrock interface eventually exceeds that within the rock mass of the HFB. In the numerical simulation, before 40 dry-wet cycles, the damage zone is concentrated near the shear band above the highest water level. Afterward, it concentrates around the fractures on both sides of the dangerous rock mass. The sensitivity of different shear band characteristics to the stability of the dangerous rock mass is ranked as follows: the height-length ratio of the shear band-bedrock interface, followed by the filling material thickness, dip angle, width, and degree of fragmentation. The findings can provide valuable reference for the stability and prevention of such dangerous rock masses.

This research introduces a powerful tool, the automatic parametrization of hardening soil (HS) model (APHS), designed to make the HS model parameterization process easier and faster than conventional methods while maintaining high accuracy. Traditional parameterizations rely on oedometer tests, unloading-reloading data, or domain-specific assumptions. Existing optimization-based models often assume uniform parameter weighting, potentially overlooking the distinct sensitivity of each parameter. APHS addresses these limitations as a standalone tool that relies exclusively on conventional triaxial loading test data. To achieve this goal and address the scarcity of labeled datasets, this study integrates numerical modeling with deep learning. The study focuses on a typical shallow Hong Kong soil with parameter ranges derived from field data and relevant literature. Latin hypercube sampling generated diverse parameter values within theoretical bounds for reliable input, while a two-dimensional (2D) axisymmetric finite element model (SIGMA/W) simulated laboratory tests to create a comprehensive, labeled dataset. Seven novel multi-parallel deep long short-term memory (LSTM) networks were trained and validated, achieving an accuracy of 99.4 %. Validation against a conventionally parameterized reference case confirmed 99.6 % accuracy, while an experimental laboratory case study demonstrated strong agreement between simulated and measured results. APHS accelerates HS model parameterization, delivering accurate results in seconds. It can seamlessly integrate with finite element models for automated laboratory data processing and physically informed models to refine calibration parameter ranges. Future work will expand its applicability to various conditions and parameters.

The stress-strain behavior of brittle siliceous mudstone coarse-grained soils (SMCGSs) under penetrating erosion critically affects the stability of SMCGS-filled embankments in erosion-prone areas, yet remains insufficiently understood, particularly regarding particle crushing and critical state behavior under low confining pressures. This study proposes a modified constitutive model to characterize erosion-induced mechanical degradation and nonlinear critical state evolution. A normalized parameterϑ, derived from the principle of crushing equivalence, is introduced to capture the coupled effects of particle breakage and critical state shifts under varying erosion intensities. Along with a nonlinear tuning index δ, this parameter is integrated into the unified hardening model for low confining pressure (UH-L), resulting in the N-UH-LE model. Consolidated drained (CD) triaxial tests under confining pressures of 100-400 kPa are conducted for model calibration and validation. The model predictions exhibit strong agreement with experimental results, with a maximum relative error of 7.76 %. The N-UH-LE model successfully reproduces key mechanical responses, including hardening, softening, shear dilation, and volumetric changes across different erosion levels. Furthermore, erosion-induced degradation decreases with lower confining pressures and higher initial void ratios (e₀ = 0.3, 0.5, and 0.7), while variations in interlocking strength (τ₀cotφ = 40 kPa, 80 kPa, and 120 kPa) show limited influence.© 2026 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Sub-level caving (SLC) is a mass mining method suitable for large, steeply dipping orebodies. The particle size distribution (PSD) of blasted material affects material flow through the stope. Improving blast-induced fragmentation can enhance draw point extraction, increasing ore recovery, reducing dilution, and lowering costs in loading and crushing. Numerical simulations using the Mechanistic Blasting Model (MBM) explored these improvements. MBM simulates the explosive loading, rock fracturing, and dynamic explosive gas effects. It addresses uneven explosive distribution from fan-shaped blast holes and complex broken ground conditions. The simulations used Ernest Henry Mine (EHM) data to define the baseline blast design and rock mass and compared field and modelled fragmentation sizes for varying explosive densities and burden sizes. Then, MBM simulations incorporated different rock mass fracture densities, tensile strengths and in-situ stresses, and further blast design changes in the blasthole diameter and charge spacings. A total of 34 scenarios were modelled. Multivariate regression analysis identified key parameters, and new regression models for P20, P50, and P80 passing sizes were developed and validated against the EHM and MBM simulation data. Additional simulations confirmed that while regression predictive models were slightly less accurate, they provided efficient predictions with acceptable accuracy.