Conventional finite element methods for large-scale numerical simulations are often constrained by high computational demands and extended runtimes. To enhance efficiency, we developed a predictive model based on a backpropagation (BP) neural network. A three-dimensional finite element model of a buried pipeline with corrosion defects crossing a reverse fault was established using ABAQUS. We systematically analyzed the effects of four key parameters—corrosion depth-to-thickness ratio, diameter-to-thickness ratio, internal pressure, and burial depth—on the seismic response of the pipeline. In this parametric study, fault displacement and the four key parameters served as inputs to the BP neural network, with the pipeline’s axial peak compressive strain as the output. The model was trained and validated using training, validation, and test datasets. Results indicate that increasing the corrosion depth-to-thickness ratio, diameter-to-thickness ratio, internal pressure, or burial depth reduces the fault displacement necessary for the lower section of the pipeline to reach its strain limit. Failure modes differ between unpressurized and pressurized pipelines, exhibiting inward local buckling and outward bulging, respectively, at stress concentration zones. The four parameters are highly correlated with the compressive strain response, with correlations transitioning from linear to nonlinear as fault displacement increases. The trained BP neural network achieves maximum prediction errors of 13.60% on the validation set and 12.84% on the test set, both below 15%, demonstrating robust accuracy and generalization in predicting the seismic response of in-service buried pipelines across reverse faults.

Significant limitations and hysteresis are presented in dynamic prediction methods driven by on-site monitored displacement data for tunnel surrounding rock deformation. By comprehensively utilizing the physical information contained in tunnel construction project documents and the mathematical information from displacement time-series curves, a modelling method based on the dynamic Bayesian network (DBN) was developed using the concept of physical information machine learning (PIML) to achieve dynamic predictions of surrounding rock deformation. Through discretization processing and reconstruction of displacement time-series curves, a static sample database was established by combining physical information data with ultimate displacement data, while a dynamic sample database was created by integrating physical information data with displacement time-series curve data. Based on the characteristics of the static samples, the K2-score algorithm was improved to construct a static Bayesian network (BN) model for ultimate displacement prediction. Utilizing the static BN model and the characteristics of the dynamic samples, physical-data dual-drive modelling methods for the Markov DBN were derived by incorporating prior information, including the constraints of steady-state random processes and Markov process constraints. By integrating prior information for constraint-enhanced optimization, the optimized Markov DBN model was established. Five-fold cross-validation tests revealed that the prediction capability of the Markov DBN model decreased rapidly over time and that the network transition direction significantly affected this capability. In contrast, the prediction ability of the optimized Markov DBN model remained robust over time, was unaffected by the network transition direction, and significantly exceeded that of the Markov DBN model, as the optimized model enhanced constraint connections between target nodes and influencing factor nodes throughout the entire timeframe. Through engineering case analysis, it was concluded that before and during the early stages of tunnel construction, the optimized Markov DBN model could effectively predict displacement time-series curves, overcoming the limitations and hysteresis inherent in traditional methods. Furthermore, during construction, self-updating of the optimized Markov DBN model and dynamic predictions of surrounding rock deformation could be achieved by inputting the on-site monitored displacement data.

When constructing tunnels in rheological strata, the creep of the surrounding rock increases the load on the supporting structure over time. Additionally, environmental influences may cause creep phenomena in the supporting structure, resulting in a complex interaction mechanism between the tunnel's surrounding rock and support due to the coupling effects of both. This article proposes an analytical method for circular tunnels based on the theory of complex functions and Laplace transform. Unlike previous analytical solutions, the approach presented here incorporates the rheological properties of the surrounding rock, non-hydrostatic stress fields, and the creep characteristics of supporting structures. The Kelvin-Voigt model was employed to simulate the rheological properties of both the surrounding rock and the supporting structures. Displacement and stress solutions were derived from the displacement coordination equation and the stress boundary conditions of the surrounding rock and support structures. The accuracy of the analytical solution was verified through numerical simulations, followed by a parameter analysis. The main conclusions drawn from this study are as follows: (1) For simple mechanical models, the analytical method proposed in this paper is faster, simpler, and retains a degree of accuracy superior to that of numerical simulations; (2) When accounting for the creep characteristics of the supporting structure, the deformation of the surrounding rock is greater compared to existing analytical results, the contact pressure between the surrounding rock and the supporting structure is reduced, and the creep of the supporting structure diminishes its bearing capacity and deformation constraint. A higher creep rate in the supporting structure correlates with a faster rate of deformation in the surrounding rock, a lower creep modulus, and increased deformation of the surrounding rock; (3) In the context of non-hydrostatic stress fields, the coupling effects of creep between the tunnel and the supporting structure can exacerbate tunnel issues such as arch uplift or inward compression of tunnel sidewalls, thereby compromising the safety of the supporting structure. Considering these factors is crucial for the design and construction of tunnels in complex environments; (4) Engineering applications demonstrate that the analytical method proposed in this paper effectively predicts the trends in tunnel surrounding rock deformation and support structure stress, showcasing its potential for practical engineering applications.

To investigate the mesoscopic crack evolution behavior and the dominant mechanisms during the failure process of sandstone, an integrated approach combining acoustic emission moment tensor inversion with RA-AF parameter analysis was employed to quantitatively characterize the types, spatiotemporal distribution, and stress response characteristics of microcracks. Based on moment tensor theory and incorporating sensor coupling coefficients calibrated through pencil-lead break experiments, microcracks were classified into five categories—shear cracks, tensile-shear mixed-mode cracks, compressive-shear mixed-mode cracks, tensile cracks, and compressive cracks—using the crack tensile angle criterion. Furthermore, an RA-AF empirical model was established to support the analysis. The results indicate the following: (1) Under various loading paths, microcracks resulting from sandstone failure are predominantly shear cracks. The number of each of the five microcrack types exhibits a positive correlation with stress level, with shear cracks showing the most significant increase. (2) As stress increases, microcracks initiate, propagate, and gradually coalesce, forming a fracture zone that corresponds to the macroscopic failure surface. (3) RA-AF analysis reveals that shear cracks account for more than 50% of all microcracks in sandstone, which aligns with findings from moment tensor inversion. (4) Waveforms generated by tensile cracks exhibit abrupt characteristics, with concentrated signal energy in the frequency domain, whereas waveforms associated with shear cracks display oscillatory behavior, featuring dispersed frequency-domain energy and higher amplitude. This distinction provides a physical mechanism that explains the heterogeneity observed in RA-AF parameters. (5) Moment tensor inversion is well-suited for theory-driven, detailed analysis of crack mechanisms, while RA-AF analysis is more appropriate for rapid identification of crack types in engineering practice. This study elucidates the dominant micromechanical mechanism of shear failure in sandstone and the co-evolutionary behavior of multiple crack types, thereby providing a theoretical foundation for rock fracture prediction.

Accurately predicting the dynamic evolution of permeability during CO₂ injection into shale reservoirs is crucial for carbon sequestration and enhanced shale gas recovery. However, traditional permeability models often fail to comprehensively describe the full-range evolution of permeability throughout the entire CO₂ injection process in shale—from the low-pressure gaseous state to the supercritical state. To address this limitation, this study develops a shale permeability evolution model based on a dual-elastic system comprising both the matrix and fractures, determined by component permeability weighting. By incorporating key factors such as mechanical degradation of the matrix, secondary adsorption, and strain hysteresis effects, we establish a governing equation for permeability evolution under multi-effect coupling. Utilizing an overlapping dual-elastic medium structure, we perform parallel cross-coupling numerical solutions, achieving an accurate representation of the nonlinear permeability evolution during full-pressure CO₂ injection. Furthermore, a decoupled analysis of influencing effects reveals that the degradation of mechanical parameters of the matrix material due to CO₂ defines the boundary thresholds for permeability fluctuation ranges. The asynchronous response between mechanical strain and adsorption strain significantly amplifies differences across evolutionary stages, leading to clearly distinguishable phase transitions. Additionally, the strain hysteresis effect prolongs the duration of evolution. Gas adsorption and mechanical responses jointly regulate the transition points between evolutionary stages, with the secondary adsorption-induced swelling strain particularly enhancing phase differentiation throughout the evolution process. This study also provides an in-depth analysis of the fundamental framework of fluid-solid coupled permeability modeling and explores the characteristics of different numerical simulation methods. The findings not only deepen the understanding of shale permeability evolution during CO₂ injection but also offer valuable insights for theoretical modeling and numerical simulation of permeability in geological fluid sequestration.

Current research on tunnels crossing active faults primarily focuses on individual tunnel cases, while the group tunnel effect in tunnel groups has not been systematically addressed. The influence of high internal water pressure on deformation mechanisms is rarely considered. This study employs physical model tests and numerical analysis under high internal pressure to investigate the fault resistance of tunnel groups. The results demonstrate the following: (1) Corrugated expansion joints significantly enhance fault resistance, delaying and reducing peak longitudinal strain (with maximum tensile strain reduced by 69% and compressive strain by 48%) and converting shear failure into coordinated deformation. (2) Group effects intensify the fracturing of surrounding rock during dislocation, resulting in a complex “Y-shaped intersecting crack system.” (3) The sides of adjacent tunnels exhibit higher strain responses than the outer sides (with peak compressive strain at 87% and longitudinal tensile strain at 35%), indicating tunnel-rock-tunnel interaction. (4) Earth pressure between tunnels increases abnormally due to group effects, while the pressure on the outer sides remains largely unaffected. (5) The mechanical response of the lining (axial and shear force) strengthens with smaller tunnel spacing but diminishes and stabilizes as spacing increases. This study reveals the failure mechanisms of high-pressure hydraulic tunnel groups, providing insights for fault-resistant designs in seismic zones.

Geothermal energy, a renewable resource with immense potential, has garnered significant attention. In deep geothermal reservoirs, artificially stimulated fracture networks serve as critical pathways for heat extraction. Consequently, the permeability and spatial distribution of these fractures directly impact heat extraction efficiency. This study conducted shear-seepage experiments on a single rough granite fracture under varying confining pressures, shear displacements, and fracture roughnesses. A nonlinear relationship between permeability and the aforementioned three factors was established based on the experimental results. This relationship was then integrated into the THM coupled framework TOUGH2MP-FLAC^3D to assess the long-term performance of Enhanced Geothermal Systems (EGS) under varying fracture networks, fracture densities, and horizontal stress ratio conditions. The findings reveal that fracture permeability exhibits an exponential negative correlation with confining pressure, a logarithmic positive correlation with shear displacement, and a quadratic correlation with fracture roughness. Increased fracture density significantly enhances thermal performance; as fracture densities increase from 0.1 to 0.25, the thermal breakthrough time extends by up to 6.4 years, the EGS lifespan increases by up to 13 years, and total heat production rises by approximately 22.5%. Horizontal stress anisotropy negatively affects thermal performance, while higher fracture density effectively mitigates the reduction in heat extraction caused by stress anisotropy. This work provides a theoretical foundation for hydraulic fracturing during the stimulation of hot dry rock reservoirs.

Ice avalanches are a primary trigger for glacier-related disaster chains in high-mountain regions. Understanding how boundary conditions influence the dynamics and deposition of ice avalanche debris flows is crucial for deciphering the evolution of such disaster chains. This study systematically investigates the motion and depositional behavior of ice avalanche debris flows under varying mass, elevation differences, slope gradients, and toe constraints, utilizing a chute-based experimental setup within a low-temperature laboratory. Key parameters, including flow velocity, basal force, and deposition morphology, are analyzed throughout the debris flow movement. Results indicate that elevation differences and mass govern the dynamic energy transfer within the flows. Specifically, elevation differences control depositional dispersion by regulating peak flow velocity, while mass influences travel duration, resulting in a positive correlation between run-out length and deposit thickness. Furthermore, topographic conditions significantly affect energy dissipation during deposition. An increased slope gradient in the run-out zone reduces basal resistance, thereby expanding the depositional area and enhancing particle scattering at the flow front. A wider slope toe promotes lateral spreading, increasing travel distance and shifting the mass center, which transforms deposit morphology from tongue-shaped to fan-shaped. Finally, theoretical analysis confirms that run-out distance is dictated by the efficiency of kinetic energy transfer among particles and their interaction with the substrate, exhibiting a positive correlation with both particle energy-transfer efficiency and fluctuations in basal stress.

To identify the distribution differences between fractures and interlayer gravels within glutenite cores, accurately evaluate spatial heterogeneity, and enhance hydrocarbon recovery efficiency, this study employed elastic ultrasonic wave velocity—a parameter highly sensitive to variations in the internal rock structure—to measure wave velocities in 18 outcrop cores collected from the Shawan Sag in the Junggar Basin. A non-destructive evaluation method for heterogeneity was established based on stratified elastic wave velocity measurements. Fifteen samples were utilized as test cores for heterogeneity assessment using this method, while the remaining three served as validation cores, with their velocity distributions compared for verification. The results demonstrate that: (1) the proposed method effectively identifies heterogeneity characteristics, such as gravel distribution and pore-fracture networks in glutenite, enabling an accurate assessment of spatial heterogeneity; (2) the method offers several advantages over conventional heterogeneity evaluation techniques, including non-destructiveness, high sensitivity, rapid measurement, and cost-effectiveness; and (3) consistent heterogeneity evaluation results were obtained between the test and validation cores. Therefore, this method can serve as a valuable reference for heterogeneity assessment in glutenite reservoirs.

This paper investigates the effect of anisotropic stress states on the small strain stiffness of red mudstone fill material (RMF). A comprehensive experimental program was conducted, including 18 triaxial-bender element tests, 4 isotropic consolidation tests, and 6 stress-controlled loading-unloading tests. The results indicate that the normalized strength is well characterized by the nonlinear strength envelope. Under isotropic stress conditions, the small strain stiffness increases with mean stress, which can be described by a power equation. During conventional triaxial shear, small strain stiffness increases at low axial strains. When the axial strain exceeds 2%, the damage point can be identified, at which point small strain stiffness decreases by more than 25% with further axial strain. A power model has been employed to characterize the small strain stiffness and shear stress at both the damage point and peak point. Unloading at stress states below the damage point results in an increase in small strain stiffness. Conversely, due to irreversible structural disturbance, unloading at stress states above the damage point leads to a progressive reduction in small strain stiffness. The difference in small strain stiffness at various unloading points can exceed 30%. Therefore, the coupled effects of stress history and stress path should be considered for accurate determination of small strain stiffness, as the conventional monotonic model is not applicable in such coupled scenarios.