Search Results (546)

Earthquake-induced accidents involving buried oil and gas pipelines in water-network regions are governed by coupled seismic, hydrological, geotechnical, and emergency-response factors, while complete accident records are scarce. To support scenario-based consequence analysis under sparse-data conditions, this study develops an accident scenario analysis framework that integrates numerical simulation with Bayesian probabilistic inference. Scenario elements are organized according to four categories: disaster-causing factors, elements at risk, hazard-inducing environment, and emergency management. Finite element analysis and computational fluid dynamics are used to quantify pipeline mechanical response and hydraulic-scour effects, and the resulting physical responses are embedded in a dynamic Bayesian network as state evidence and transition constraints. Triangular fuzzy numbers are used to process expert evaluations and determine node probabilities. The resulting multi-mechanism simulation-Bayesian inference framework quantifies the accident chain from earthquake loading to pipeline deformation, leakage, fire or explosion, and emergency control. Forward reasoning estimates the probability of each scenario state, sensitivity analysis identifies key drivers, including strong earthquakes triggering landslides and rainfall during flood seasons, and disaster-chain analysis clarifies the dominant causative pathways. The framework provides a reproducible basis for scenario analysis, consequence assessment, monitoring and early warning, and emergency response planning for buried oil and gas pipelines exposed to seismic hazards in water-network regions. Full article

The Yarlung Zangbo River region is characterized by complex geological structures and distinctive physiographic environments, where landslide deposits are susceptible to deformation and instability under internal and external forces. Therefore, determining their potential movement processes, kinematic characteristics, and failure modes is crucial for landslide hazard prevention and mitigation. Taking the Yuqiong landslide deposit as a case study, this paper employed field investigation, laboratory testing, and numerical simulation to model its instability and failure process, as well as analyze its movement characteristics and the failure mechanism. The main results are as follows: (1) The entire sliding process of landslide instability and failure lasts approximately 100 s. The deformation and instability process can be divided into four stages: initiation and sliding, deformation propagation, deformation accumulation, and cessation. The velocity evolution comprises three stages: start-up acceleration (accounting for 10%), rapid deceleration and slow deformation (together accounting for 90%). The initiation of the crown exhibits a certain degree of suddenness. (2) The energy distribution during landslide instability is controlled by topographic slope, sliding mass thickness, and travel distance. At the crown, where the slope is steep, the thickness is large, and the travel distance is short, frictional energy dissipation is low, resulting in concentrated energy. In contrast, at the toe and the middle part, where the slopes are gentle and the travel distances are long, energy dissipation is high, leading to lower energy distribution. (3) The deformation and failure mechanism of the landslide is characterized as follows: active thrusting at the crown drives the movement; the toe, owing to the gentle slope and thick layer that provide high sliding resistance, undergoes slow retrogressive buffering; the middle part is subjected to both pushing and pulling, resulting in settlement and stress transfer. Overall, the landslide exhibits a composite progressive failure mode combining crown thrusting and toe retrogressive action. Full article

Landslide susceptibility assessment acts as a core technical tool for geological disaster governance, ecological protection and long-term risk mitigation strategies. This modeling approach quantifies the possibility of slope-collapse events and delivers objective decision-making support for regional geologic environment supervision. To overcome the low computational efficiency and weak capacity of conventional evaluation frameworks to extract multi-level spatial grid rules, this paper takes Nanning City, the capital and largest city of the Guangxi Zhuang Autonomous Region in southern China, as the research object. Ten types of terrain and geological control factors combined with historical landslide inventory records are adopted to build a two-stage coupled evaluation framework integrating the information value method (IVM), a Bidirectional Temporal Convolutional Network (BiTCN) and Transformer, named IVM-BiTCN–Transformer. The hierarchical framework first adopts IVM to finish preliminary hazard grading and calculate factor contribution weights, then inputs classified grid samples into the BiTCN-Transformer module to realize local terrain feature and global factor fusion, which significantly lifts the overall identification precision. Ten widely adopted landslide evaluation algorithms are selected for contrast simulation, with multiple quantitative metrics adopted to judge model reliability. Experimental outcomes prove that the presented IVM-BiTCN–Transformer framework obtains superior hazard discrimination capacity, which can raise the precision and stability of landslide zoning and offer reliable technical support for targeted regional geological disaster prevention. Full article

Slope stability has consistently been a critical concern in mountainous road sections, with precipitation being the most significant factor precipitating slope instability. This study aims to elucidate the mechanism of slope instability under precipitation conditions and the extent of the impact of internal disaster-causing factors. To achieve this objective, a numerical simulation analysis method combining GeoStudio2018R2 and FLAC3D7.0 software was employed to conduct a comprehensive analysis of an unstable slope in Xinjiang. Regarding research methodology, cyclic precipitation and seasonal snowmelt were considered as external influencing factors. Initially, a two-dimensional model was constructed using GeoStudio software to analyze the spatial and temporal variations in pore water pressure and moisture content within the slope, elucidating their dynamic characteristics at different temporal and spatial scales. Subsequently, a three-dimensional numerical model was established using FLAC3D software to conduct a detailed analysis of the stress–strain state of the slope under various conditions, thereby obtaining disaster parameters such as displacement and sliding velocity in different directions. Through further comparison and verification of the overall stability analysis results of the slope obtained from both software packages, it was observed that they exhibited a consistent trend. The research findings indicate that under conditions of high-intensity short-term precipitation, the safety factor of the slope decreases to the lowest level, potentially leading to shallow landslides with smaller displacement but faster sliding velocity. Conversely, seasonal snowmelt and long-term localized precipitation have a more profound impact on the internal structure of the slope, with the sliding zone potentially penetrating into the deep bedrock. Although the occurrence frequency is low, the impact range is extensive. By combining two-dimensional and three-dimensional analyses, a comprehensive assessment of the different disaster-causing factors of the slope was conducted, enhancing the accuracy of the analysis results. The research findings provide a scientific basis and reference value for the formulation of subsequent slope protection and monitoring plans. Full article

Rapid landslide motions control impact area, flow velocity, deposition pattern, and, in extreme cases, are a river-blocking hazard; therefore, reliable dynamic simulations are of direct importance to engineering–geological hazard assessments. Depth-averaged models provide an efficient framework for simulating large-scale mass movements, but conventional physics-informed neural networks (PINNs) remain challenged with regard to nonlinear flows, which can limit their applicability in landslide analysis. To address these limitations, this study develops a physics-informed residual convolutional network model (PI-RCN) for depth-averaged landslide dynamics. The proposed framework combines sequential residual learning with depth-wise separable convolutions (DSCs) and incorporates physics-based residuals, mass conservation, and hard constraints to preserve physical consistency during time marching. The model is evaluated using a 1+1D frictionless dam-break benchmark, a Hong Kong landslide, and the Yigong rock avalanche. Results show that PI-RCN accurately reproduces the benchmark flow evolution with substantially fewer trainable parameters than a baseline fully connected PINN. In the Hong Kong case, the model demonstrates improved convergence stability and optimization efficiency. In the Yigong case, PI-RCN reproduces the main spatiotemporal evolution and multi-stage velocity variation of a long-runout rock avalanche. These results suggest that PI-RCN provides a useful physics-informed framework for efficient and consistent landslide dynamic simulation. Full article

Accurate characterization of subsurface stratigraphy is essential for reliable landslide stability assessment. However, stratigraphic models constructed solely from sparse borehole data are often constrained by incomplete spatial coverage and substantial interpretive uncertainty. To address this issue, this study developed an integrated probabilistic stratigraphic modeling framework that combines borehole data with electrical resistivity tomography (ERT) data. In the proposed framework, borehole logs provide direct lithological labels and spatial prior information, while the inverted ERT resistivity profile is introduced as a continuous geophysical constraint. Specifically, logarithmic resistivity and the borehole-derived expected stratigraphic configuration were combined into a support vector machine classifier to establish a nonlinear mapping between geophysical responses and stratigraphic categories. A bootstrapping strategy was also used to quantify the stratigraphic uncertainty. The proposed method was then applied to the Panzhuangzu Landslide in Henan Province, China. Based on the probabilistic stratigraphic models, multiple plausible stratigraphic realizations were generated, and their corresponding stability responses are evaluated through numerical analysis. Monte Carlo simulations were further performed to examine how stratigraphic uncertainty propagates into landslide stability predictions. The results show that incorporating ERT data improves the geological plausibility of the inferred stratigraphy. Compared with the borehole-only case, the results obtained from the integrated framework exhibited reduced uncertainty in both the inferred stratigraphic model and the corresponding landslide stability assessment. These findings indicate that the proposed borehole–geophysical data fusion method can provide a more reliable geological basis for landslide stability analysis. Full article

Landslides constitute one of the most lethal and costly hydrometeorological hazards at the global scale. There is a growing trend associated with the increase in extreme precipitation and the expansion of urban development on unstable slopes. In the tropical Andes, this problem is intensified under climate change scenarios. The objective of this study is to develop a logistic regression model to analyze socioeconomic risk due to landslides in the Bogotá Savannah (Colombia). An integrated risk model was developed using binary logistic regression and a socioeconomic vulnerability index. A total of 12 physical–biotic variables and SSP climate projections (2021–2040) were used. A GIS-based environment was implemented to generate prospective spatial risk scenarios. The model demonstrated high robustness and predictive capability, with an improvement in statistical goodness-of-fit of 8.2% (AIC: 2574–2367), adequate probabilistic calibration (Pseudo-R²: 0.675; Brier Score: 0.084), and excellent predictive performance (AUC: 0.935; sensitivity: 84.7%; specificity: 90.0%). Simulations estimated maximum risk probabilities close to 0.600 (scale between 0 and 1), concentrated in geomorphologically critical sectors. Simulations under SSP scenarios showed a progressive increase in risk toward 2040 (up to 0.673), associated with precipitation increases between 10 and 30%. Integrated modeling constitutes a reliable technical tool for land-use planning, climate adaptation, and prospective landslide risk management in urbanized Andean regions. Full article

Landslide susceptibility is often treated as a static assessment of present-day conditions, overlooking the temporal evolution of geomorphological and anthropogenic drivers. This limitation is particularly relevant in rapidly urbanising areas, where land use change continuously alters environmental conditions influencing slope stability. This study examines the temporal evolution of landslide susceptibility in the Grigorescu neighbourhood of Cluj-Napoca, Romania, using environmental datasets representing conditions in 1971, 2009, and 2025, along with a projected land use scenario for 2047. The proposed framework integrates multi-temporal landslide inventories and conditioning factors with Maximum Entropy (MaxEnt) modelling and CA–Markov–MLP land use simulation (MOLUSCE). Results indicate a progressive shift towards higher susceptibility classes over time, accompanied by urban expansion onto increasingly steep terrain. However, slope gradient remained the dominant conditioning factor throughout all analysed periods, while land use change influenced the temporal evolution and spatial redistribution of susceptibility through progressive urban expansion into terrain already predisposed to instability. The 2047 scenario suggests that continued urban expansion may increase the exposure of built-up areas to zones of elevated susceptibility. Model performance was robust (AUC > 0.8; Kappa > 0.9). Beyond site-specific findings, the framework provides a transferable methodology for integrating urban growth dynamics into landslide susceptibility assessment, supporting sustainable spatial planning and risk-informed urban development in rapidly urbanising hilly environments. Full article

Rainfall is a critical factor inducing slope instability, and accurate prediction of the factor of safety (FOS) of slopes under rainfall conditions is of paramount importance for disaster prevention and mitigation. Conventional numerical simulation methods incur high computational costs, while individual machine learning models are often insufficient to adequately capture the nonlinear spatiotemporal evolution characteristics of multiple factors under coupled multi-physics fields. To address these limitations, this paper proposes a Transformer–LSTM prediction framework. First, a fluid–structure coupling model for rainfall-affected slopes is constructed using COMSOL, and multi-factor orthogonal experiments are performed to generate multi-dimensional time-series data. Subsequently, a Transformer–LSTM fusion deep learning model is built, in which LSTM is employed to extract the temporal dynamic characteristics of rainfall infiltration, and the self-attention mechanism of the Transformer is leveraged to enhance feature extraction and global dependency modeling of key disaster-causing factors. Experimental results demonstrate that the Transformer–LSTM model significantly outperforms traditional PSO-LSTM, PSO-SVM, and standalone Transformer or LSTM models in terms of both prediction accuracy and generalization capability. Its coefficient of determination (R²) remains above 0.94, and key evaluation metrics—including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)—attain the lowest values among the compared models. Furthermore, the SHAP (SHapley Additive exPlanations) interpretability framework is introduced to quantitatively elucidate the model’s predictive decision-making and to establish a physically grounded causal mapping with geotechnical mechanisms. It is confirmed that effective cohesion and slope angle exert a dominant interactive effect on the degradation of slope stability, providing data-driven support for wide-area monitoring of rainfall-induced landslides. Full article

Bench slopes in open-pit mines are highly susceptible to progressive deformation and instability due to the coupled effects of excavation disturbance, rock mass weathering, and extreme rainfall, posing significant challenges to rapid risk assessment and engineering decision-making. To address the limitations of conventional methods in efficiency and adaptability under complex multi-factor conditions, this study proposes a hybrid simulation–artificial intelligence framework for rapid slope stability assessment and bench face angle optimization. Multi-scenario numerical simulations were conducted by integrating geological investigation data, laboratory and in situ mechanical parameters, and extreme rainfall conditions to characterize slope deformation and failure mechanisms and generate a dataset for machine learning model training. Machine learning models were trained using slope height, bench face angle, unit weight, cohesion, and friction angle as inputs, and safety factors under natural and extreme rainfall conditions as outputs, with hyperparameters optimized by Bayesian optimization. The results indicate that highly weathered rock masses dominate shallow deformation and act as critical weak zones, while extreme rainfall significantly accelerates instability evolution and reduces slope safety factors. Among the RF, SVR, and ELM models, the Bayesian-optimized support vector regression (BO-SVR) exhibits the best predictive performance (R² > 0.98). SHapley Additive exPlanations (SHAP) analysis reveals that slope height and shear strength parameters are the dominant controlling factors, whereas unit weight has a relatively limited influence. Validation using real landslide cases shows good agreement with numerical simulations, confirming the reliability of the proposed framework. The developed approach enables rapid risk evaluation and supports bench face angle optimization, providing an effective tool for intelligent slope management in open-pit mining. Full article

The stability and functionality of offshore wind energy systems depend critically on how offshore platforms interact with the geotechnical features of the seabed. This review describes developments in five areas: (i) offshore geotechnical site investigation and strength assessment; (ii) seabed geohazard causes and deep-water mooring challenges; (iii) frameworks for seabed modeling; (iv) sediment behavior influencing anchor and mooring performance; and (v) selection of anchors based on their interactions with various soils. The review emphasizes developments in seabed assessment and modeling using field, lab, and numerical methods. It discusses how the new advances in analytical and simulation frameworks have enhanced our knowledge of anchor–mooring responses, cyclic loading behaviors, and soil–structure interactions under changing seabed conditions. The key findings reveal that: (1) cyclic loadings considerably change anchor holding capacity and evolution of seabed trenching, yet most existing design methods still use quasi-static loads; (2) site-specific data from integrated geophysical–geotechnical surveys are vital to reduce uncertainty in anchor penetration and the frictional resistance of chains; (3) geohazards, such as shallow gas, marine landslides, and seabed erosion, pose under-recognized risks to long-term anchor reliability. The lack of knowledge on the coupled, long-term evolution of the seabed–anchor–mooring line system is identified as another gap in the literature. Major gaps exist in validating the life cycle of anchor performance under real-scale storm–wave sequences for offshore geotechnical risk management in layered soils. At the end of the discussion, the current study also highlights the need for flexible, data-driven frameworks that integrate geotechnical, hydrodynamic, and structural analyses in a coupled framework to improve reliability in next-generation offshore wind energy systems. Full article

Wildfires may significantly alter the mineralogical and microstructural characteristics of geological materials, leading to increased susceptibility to landslides, debris flows, and other related hazards. These processes may involve considerable post-fire hydrological changes that affect the infiltration rate and the surface runoff in the burned soils. In the present study, a laboratory experimental investigation is carried out focusing on the water infiltration in burned soils which were produced in a muffle furnace at accurately controlled temperatures within 400 °C∼800 °C. The original and burned soils were first subjected to a number of geotechnical tests, including grain size distribution, consistency, and hydraulic conductivity. Subsequently, their water infiltration rates were measured in a laboratory setup. Finally, numerical simulations are performed to assess the infiltration process based on the Green–Ampt model. The experimental results reveal significant differences in the hydrological behavior between burned and unburned soils. Overall, burned soils experienced quicker ponding and slower infiltration. However, as the burning temperature increased from moderate to high, the infiltration rate also rose considerably, along with delayed ponding time. This trend may be related to the microstructural change in the grain size distribution explored experimentally in the present study. The numerical results are highly consistent with the experimental data. The hydraulic conductivity is identified as the predominant parameter in the infiltration process examined and simulated in the present study. Its evolution with varied burning temperatures can also be traced to the fire-induced alteration in the grain size distribution, and primarily accounts for the differences in the infiltration of different soil specimens. The present study demonstrates the potential of laboratory experiments complemented with a quantitative modeling approach in improving our understanding of soil’s post-fire hydrological responses. Full article

Given the escalating impacts of global climate change and extreme weather events, the accurate stability assessment of rainfall-induced landslides necessitates a comprehensive consideration of both seepage processes and the inherent spatial variability of soils. Traditional deterministic and two-dimensional (2D) analyses often fail to capture the multi-dimensional kinematic features of slope failures and the stochastic nature of soil heterogeneity, thereby leading to inaccurate risk assessments. This study proposes a three-dimensional (3D) slope reliability analysis framework. Within this framework, a 3D slope geometric model is constructed using GeoStudio 2025.1.0 software, and seepage analysis is conducted by the SEEP3D module. To account for soil spatial variability, the Karhunen–Loève (K-L) expansion method is employed to discretize key shear strength parameters (effective cohesion and effective angle of internal friction). The factor of safety (F_s) is evaluated using the 3D simplified Bishop method, which is then coupled with Monte Carlo simulations to determine the probability of failure (P_f). The results show that rainfall infiltration causes progressive dissipation of shallow matric suction and a significant rise in the groundwater table near the slope toe, resulting in reduced effective stress in the critical resistance zone. As rainfall intensity increases, the F_s decreases approximately linearly from 1.14 to 0.90, whereas the P_f increases nonlinearly from nearly 0 to 98.36%. Under the rainstorm condition, although the F_s remains above unity at 1.063, the corresponding P_f reaches 23%, indicating that deterministic evaluation based only on the F_s may underestimate the actual failure risk. The proposed framework provides a quantitative tool for evaluating rainfall-induced slope instability by integrating transient hydraulic response, three-dimensional spatial variability, and probabilistic reliability assessment. Full article

Highway slopes susceptible to landslides are typically reinforced by vegetation cover and the application of concrete frame beams, but vegetation cover may degrade the accuracy of InSAR deformation monitoring. We installed artificial corner reflectors (CRs) on the frame beams and assessed the stability of the vegetated slope using finite element simulation constrained by InSAR deformation data. A study was conducted on a typical landslide-risk slope within the K87 + 391.5–K87 + 565 section of the Guihuang highway, which is reinforced with cast-in-place and prefabricated concrete beams. Experimental results demonstrate that two adjacent corner reflectors (CRs) on the two types of frame beams of the slope can be successfully identified, with deformation rates ranging from 0.1 to 0.4 mm/y, and the root mean square error (RMSE) of discrepancies between CR-InSAR measurements and slope displacement monitoring sensors is less than 0.3 mm. Meanwhile, the current strength reduction factor values for slopes reinforced with cast-in-place and prefabricated concrete beams, as constrained by InSAR multi-dimensional deformation, are 0.11 and 0.12, respectively which are much lower than the critical strength reduction factors of 1.28 and 1.22 corresponding to full coalescence of plastic strain from the slope toe to the slope crest, which indicates that the cast-in-place and prefabricated frame beams exhibit comparable support performance. Full article

The Yanshangou landslide, located in the Baihetan Reservoir area, poses severe potential threats to the normal operation of the reservoir due to its distinct deformation characteristics and high sensitivity to reservoir water level fluctuations. This study systematically investigates the geological background, deformation characteristics, stability evolution, and landslide-induced surge hazards of the Yanshangou landslide in the Baihetan Reservoir area. This work only considers the influence of reservoir water level fluctuations, which is the dominant factor controlling the current progressive deformation of the landslide. Field surveys and GNSS/deep displacement monitoring results revealed that the Yanshangou landslide exhibits obvious staged deformation characteristics, and the landslide deformation rate was closely coupled with the dynamic changes in reservoir water level. A slope stability evaluation method integrating the Morgenstern–Price limit equilibrium method and Richard’s equation was established, and the results indicated that the Yanshangou landslide has low saturated permeability. Therefore, its factor of safety (FOS) presents a clear four-stage variation trend in response to reservoir water level fluctuations. A Smoothed Particle Hydrodynamics (SPH)-based numerical model was further developed to simulate the landslide-induced surges under two typical reservoir water level scenarios (815 m and 765 m). The simulation results demonstrated that a high reservoir water level led to more intense surges with greater height and higher velocity, while a low reservoir water level resulted in surges with a wider propagation range along the reservoir bank. The research findings of this study provide a comprehensive theoretical basis and detailed data support for the prevention and mitigation of geological hazards in the Baihetan Reservoir area, and also offer a reference for the hazard management of similar reservoir landslides worldwide. Full article