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.

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.

Compacted bentonite blocks are proposed for buffer barriers in deep geological repositories for high-level radioactive waste (HLRW) disposal. These blocks, manufactured through uniaxial compression in molds, exhibit heterogeneity that may impact long-term buffer performance. This study focuses on the physical and hydro-mechanical heterogeneity of full-scale blocks induced by the compaction process. Sector-shaped blocks, with radii of 600 mm and 1200 mm and a height of 200 mm, were axially compressed. Key parameters, including water content, dry density, elasticity modulus, swelling pressure, and permeability, were measured to assess the heterogeneity. Results show that the heterogeneity in the upper layer is primarily caused by differences in drainage and gas expulsion pathways. As depth increases, water content and dry density become more correlated. Hydro-mechanical behavior is largely controlled by dry density, but its fluctuation ratio is much higher than that of dry density. Regarding the microstructure, pore structure heterogeneity follows the order: corner regions > edge regions > center regions, and upper layer > middle layer > lower layer. Vertical microcracks also develop to varying degrees, increasing the anisotropy of the blocks. Upon these observations, the study thoroughly discusses the feasibility and challenges of reckoning the hydro-mechanical properties of blocks using dry density distribution alongside laboratory-scale data. Additionally, it proposes an indicator to evaluate the overall heterogeneity of buffer blocks. These findings highlight the inherent heterogeneity of compacted bentonite blocks at the engineering scale, providing valuable insights for future experiments and simulations.

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.

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 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.

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.

Landfill cover system plays a crucial role in reducing leachate generation by limiting rainwater infiltration. This paper evaluates the field performance of a polymer-enhanced three-layer cover system at a leather sludge dump site in Xinji city, China over a 1-year monitoring period. Waste soil (WS), sand-bentonite mixture (SB), and sand-polymer-bentonite mixture (SPB) were used as the low-permeability layer, respectively, in three test areas, above which the fine-grained cultivated soil and gravel were used in the top and middle layers to form a capillary barrier. During the 1-year monitoring period, the recorded cumulative rainfall was 452.1 mm, and the volumetric water content (VWC) at the top layer fluctuated significantly from 0.13 to 0.45 in response to rainfall and evaporation, but that of the low-permeability layer maintained stable for both cover SB and SPB. No water percolation was detected during the 1-year monitoring period. Furthermore, numerical simulations were carried out to assess the anti-seepage performance under more extreme climatic conditions (i.e., higher rainfall intensity and long-term deterioration of soil permeability). Numerical simulations corroborated the field observations that the SPB layer effectively minimized percolation even under extreme climatic conditions. For example, under the most unfavourable conditions, the computed annual percolation through the cover SPB was 4.7 mm, as low as 27.2% and 8.1% that through the cover SB (=17.3 mm) and WS (=57.9 mm). Overall, the results suggest that the polymer-enhanced three-layer soil cover is a promising alternative to traditional geomembrane-based covers and/or thick composite soil covers.

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.

Thermal cycling and stress fatigue are recognized as principal factors that induce the Kaiser effect of rock in deep earth rock engineering. Nevertheless, existing scholarly investigations about the mechanical properties of rocks subjected to the synergistic effects of these perturbations have remained insufficient. In this study, conventional triaxial compression tests, multistage equal-amplitude fatigue (MEF) and multistage variable-amplitude fatigue (MVF) tests were conducted on marble subjected to different numbers of thermal cycles, integrated with nuclear magnetic resonance (NMR) and depth-sensing indentation (DSI) micro-monitoring methods, and the rock constitutive equation was established from the perspective of statistical microscopic damage. The results indicated that the increasing number of thermal cycles significantly weakened the physical and mechanical properties of marble, as evidenced by degradations in strength, deformation, and energy parameters. The reversible deformation evolutions of the rock under two stress paths were diametrically opposed. DSI results revealed that the microcellular mechanical parameters of hornblende and dolomite exhibited greater variability, although both conform to Weibull distribution functions. Additionally, NMR analysis showed that the porosity of the marble was 1.6% initially and increased to 3.3%, 4.1%, 5.8%, and 10.9% after 2, 4, 6, and 8 thermal cycles, respectively. The coupled thermal-mechanical damage constitutive model can effectively describe the deformation behavior of marble under complex perturbations, with distribution parameters m₀ and T₀ decreasing linearly with the number of thermal cycles.