Search Results (221)

Slope stability monitoring and evaluation are key means to ensure the safety of engineering projects. Firstly, the classification, principles, and characteristics of distributed fiber optic sensing technology for slope engineering are introduced, and the significant advantages of this technology in slope monitoring are analyzed. Secondly, taking the Three Gorges Reservoir landslide as a case study, laboratory experiments of slopes were conducted using spatiotemporally continuous fiber optic neural sensing technology. Through the slope physical model experiment under loading excavation and rainfall conditions, it is found that (1) the strain changes monitored by vertically laid sensing cables are more sensitive to loading (with a peak strain of about 1400 με), while horizontally laid optical cables are more sensitive to excavation processes (with a peak strain of about 8900 με). Specifically, the tension–compression strain transformation in horizontally laid sensing cables can be used to identify slope failure in advance. (2) Rainfall infiltration significantly weakens the strength of the slope soil. Only considering the loading situation, the slope experiences instability and failure under a load of 120 kg. Under the premise of the soil saturation caused by rainfall infiltration, the slope experienced instability and failure under a load of 20 kg. Therefore, compared to human engineering activities, rainfall has a more significant impact on the stability of the slope. This study sheds light on the slope failure mechanism and provides a scientific basis for early warning. Full article

This study examines the deformation and failure mechanisms of two reservoir bank landslides: the traction-type Baijiabao landslide and the translational Baishuihe landslide. Based on long-term monitoring data and a hydro-mechanical coupled numerical model of rainfall infiltration, we investigate the impact of crack depth on landslide stability. Results show that the Baishuihe landslide exhibits translational failure, initiated at the rear by tension cracks and rear subsidence, followed by toe uplift, whereas the Baijiabao landslide displays traction-type progressive failure, starting with toe erosion and later developing rear-edge cracks. Rainfall induces similar seepage patterns in both landslides, with infiltration concentrated at the crest, toe, and convex terrain areas. As crack depth increases, soil saturation near the cracks decreases nonlinearly, while the base remains saturated. However, displacement responses differ: Traction-type landslides exhibit opposing lateral movements with minimal vertical displacement. In contrast, translational landslides show displacement increasing with crack depth, dominated by gravity. These findings guide targeted mitigation: traction-type landslides require crack control and toe protection, while translational landslides need measures to block thrust transfer and monitor deep slip surfaces. This study offers new insights into the effect of crack depth on landslide stability, contributing to improved landslide hazard assessment and management. Full article

Landslide susceptibility assessment constitutes a pivotal method of preventing and reducing losses caused by geological disasters. However, traditional models are often influenced by subjective grading factors, which can result in unscientific and inaccurate assessment outcomes. In this study, we thoroughly analyze various landslide causative factors, including geological, topographical, hydrological, and environmental components. A quantitative continuous model was employed, with methods such as frequency ratio (FR), cosine amplitude (CA), information value (IV), and certainty factor (CF) being applied in order to assess the landslide susceptibility of the Wanzhou coastline in the Three Gorges Reservoir area. The results were then compared with methods such as Bias-Standardised Information Value (BSIV), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosted Decision Tree (GBDT). This process led to the following key conclusions: (1) Most landslide susceptibility zones are predominantly banded and clustered on both sides of the Dewuidu River, particularly along the left bank of the Yangtze River from Dewuidu Town to Wanzhou City, as well as in the main urban area of Wanzhou. Clusters of the Yangtze River mainstem and surrounding towns characterize these areas. (2) The enhanced statistical analysis model shows a notable increase in sensitivity to landslides, achieving an Area Under the Curve (AUC) of 0.8878 for the IV model—an improvement of 0.0639 over the traditional BSIV model. This enhancement aligns closely with machine learning capabilities, and the spatial results obtained are more continuous. (3) By substituting manual grading with a quantitative continuous model, we achieve a balance between interpretability and computational efficiency. These findings lay a scientific foundation for the prevention and management of geological disasters in Wanzhou and provide valuable insights for comparable regions undertaking landslide susceptibility assessments. Full article

The Zigui Basin, located in the Three Gorges Reservoir Area, has developed numerous landslides due to its interlayering of sandstone and mudstone, geological structure, and reservoir operations. This study identifies a fourth type of landslide failure mode: an oblique-dip slope wedge (OdSW) landslide, based on the Wanshuitian landslide. Following four heavy rainfall events from 3 to 13 July 2024, this landslide exhibited significant deformation on the 17th and was completely destroyed within 40 min. The dimensions of the landslide were 350 m in length, 160 m in width, and 20 m in thickness, with a volume estimated at 8.0 × 10⁵ m³. The characteristics of landslide deformation and the changes in moisture content within the shallow slide body were ascertained using unmanned aerial vehicles, moisture meters, and mobile phone photography. The landslide was identified to have occurred within the weathered residual layer of mudstone, situated between two sandstone layers, with the eastern boundary defined by an inclined rock layer. Upon transitioning into the accelerated deformation stage, the landslide initially exhibited uniform overall sliding deformation, culminating in accelerated deformation destruction. The dip structure created terrain disparities, resulting in a step-like terrain on the left bank and gentler slopes on the right bank, with interbedded soil and rock in a shallow layer, because the interlayered soft and hard geological conditions caused varied weathering and erosion patterns on the riverbank slopes. The interbedded weak–hard stratum layer fostered the development of the oblique-dip slope wedge landslide. Based on the improved Green–Ampt model, we developed a stability prediction methodology for an oblique-dip slope wedge landslide and determined the rainfall infiltration depth threshold of the Wanshuitian landslide (9.8 m). This study aimed not merely to sharpen the evolution mechanism and stability prediction of the Wanshuitian landslide but also to formulate more effective landslide-monitoring strategies and emergency management measures. Full article

The shear strength parameters of landslide zones are the necessary data basis for landslide stability evaluation and landslide surge disaster chain research. It is important to determine the physical and mechanical parameters of landslide zones scientifically and reasonably. In this study, four small residual landslide deposits near the Hei Duo Village road in Diebu County, Gansu Province, were investigated. The research involved detailed field investigations, the construction of landslide engineering geological models, and the use of the transfer coefficient method for simultaneous/inverse inversion and sensitivity analysis of the strength parameters of the four landslides. Based on the inversion results, an analysis of the landslide formation mechanism was conducted. The inversion results yielded the shear strength parameters of the sliding surface soil as c = 30.12 kPa and φ = 21.08°. It was found that the excavation at the base of the slope is the direct triggering factor for the landslides, with the 3# landslide being the most affected by the base excavation. In terms of the type of movement, all four landslides belong to the retrogressive landslide, with the maximum shear strain increment mainly concentrated at the slope angle after excavation. The slope body experiences shear failure, which is in good agreement with the field conditions. The study provides reference for stability prediction and disaster prevention and control of reservoir bank slope. Full article

Displacement back-analysis is a crucial approach to enhance the effectiveness of landslide monitoring data. To improve the computational efficiency and reliability of large-scale three-dimensional landslide displacement inversion, this study develops a novel Landslide Displacement Intelligent Dynamic Inversion Framework (LDIDIF), which integrates the Bayesian displacement back-analysis (BBA) approach, a Long Short-Term Memory (LSTM) surrogate model, and the RANdom SAmple Consensus (RANSAC) algorithm. Specifically, BBA is employed to dynamically calibrate geotechnical parameters with uncertainty, the LSTM model replaces traditional numerical simulations to reduce computational cost, and RANSAC filters inlier observations to enhance the robustness of the inversion model. A case study of the Dawanzi GNSS landslide is conducted. Results show that the LSTM surrogate model achieves prediction errors below 2 mm and enhances computational efficiency by approximately 50,000 times. The RANSAC algorithm effectively identifies and removes GNSS outliers. Notably, LDIDIF significantly reduces the uncertainty of shear strength parameters within the slip zone, yielding a calibrated displacement precision better than 10 mm. The calibrated model reveals that the landslide is buoyancy-driven and that frontal failure may trigger progressive deformation in the rear slope. These findings offer valuable insights for landslide early warning and reservoir operation planning in the Dawanzi area. Full article

Accurate prediction of landslide-induced displacement is essential for the structural integrity and operational safety of buildings and infrastructure situated in geologically unstable regions. This study introduces a novel hybrid predictive framework that synergistically integrates Gaussian Process Regression (GPR) with Temporal Convolutional Neural Networks (TCNs), herein referred to as the GTCN model, to forecast displacement at building monitoring points subject to landslide activity. The proposed methodology is validated using time-series monitoring data collected from the slope adjacent to the Zhongliang Reservoir in Wuxi County, Chongqing, an area where slope instability poses a significant threat to nearby structural assets. Experimental results demonstrate the GTCN model’s superior predictive performance, particularly under challenging conditions of incomplete or sparsely sampled data. The model proves highly effective in accurately characterizing both abrupt fluctuations within the displacement time series and capturing long-term deformation trends. Furthermore, the GTCN framework outperforms comparative hybrid models based on Gated Recurrent Units (GRUs) and GPR, with its advantage being especially pronounced in data-limited scenarios. It also exhibits enhanced capability for temporal feature extraction relative to conventional imputation-based forecasting strategies like forward-filling. By effectively modeling both nonlinear trends and uncertainty within displacement sequences, the GTCN framework offers a robust and scalable solution for landslide-related risk assessment and early warning applications. Its applicability to building safety monitoring underscores its potential contribution to geotechnical hazard mitigation and resilient infrastructure management. Full article

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article

Early warning systems depend heavily on the accuracy of landslide displacement forecasts. This study focuses on the Bazimen landslide located in the Three Gorges Reservoir region and proposes a hybrid prediction approach combining support vector regression (SVR) and long short-term memory (LSTM) networks. These models are optimized via the K-Nearest Neighbor (KNN) algorithm. Initially, cumulative displacement data were separated into trend and cyclic elements using a smoothing approach. SVR and LSTM were then used to predict the components, and KNN was introduced to optimize input factors and classify the results, improving accuracy. The final KNN-optimized SVR-LSTM model effectively integrates static and dynamic features, addressing limitations of traditional models. The results show that LSTM performs better than SVR, with an RMSE and MAPE of 24.73 mm and 1.87% at monitoring point ZG111, compared to 30.71 mm and 2.15% for SVR. The sequential hybrid model based on KNN-optimized SVR and LSTM achieved the best performance, with an RMSE and MAPE of 23.11 mm and 1.68%, respectively. This integrated model, which combines multiple algorithms, offers improved prediction of landslide displacement and practical value for disaster forecasting in the Three Gorges area. Full article

Landslide-induced impulse waves (LIIWs) are significant natural hazards, frequently occurring in mountain reservoirs, which threaten the safety of waterways and dam project. To predict the impact of impulse waves induced by Rongsong (RS) potential landslide on the dam, during the layered construction period and maximum water level operation period of Rumei (RM) Dam (unbuilt), a large-scale three-dimensional similar physical model with a similarity scale of 200:1 (prototype length to model length) was established. The experiments set five water levels during the dam’s layered construction period and recorded and analyzed the generation and propagation laws of LIIWs. The findings indicate that, for partially granular submerged landslides, no splashing waves are generated, and the waveform of the first wave remains intact. The amplitude of the first wave exhibits stable attenuation while the third one reaches the largest. After the first three columns of impulse waves, water on the dam surface oscillates between the two banks. This study specifically discusses the impact of different water depths on LIIWs. The results show that the wave height increases as the water depth decreases. Two empirical formulas to calculate the wave attenuation at the generation area and to calculate the maximum vertical run-up height on the dam surface were derived, showing strong agreement between the empirical formulas and experimental values. Based on the model experiment results, the wave height data in front of the RM dam during the construction and operation periods of the RM reservoir were predicted, and engineering suggestions were given for the safety height of the cofferdam during the construction and security measures to prevent LIIW overflow the dam top during the operation periods of the RM dam. Full article

The creep mechanical behavior of the slip zone soil is distinctive and assumes a vital role in the identification and prediction of landslide evolution, but the rock content and structure dictate its creep properties. This study examines the Outang landslide in the reservoir region of middle Yangtze River, where the slip zone soil shows considerable variability in particle size distribution, with gravel content varying between 35% and 55%. To investigate the creep characteristics of the slip zone soil, large-scale direct shear creep tests were conducted, focusing on the variations in peak strength and long-term strength under different gravel content conditions. PFC^3D numerical simulations were subsequently performed to elucidate the internal mechanisms connecting gravel content, microstructure, and macroscopic mechanical strength. A three-dimensional continuous-discrete coupled model was built to investigate the influence of gravel content on landslide deformation features, accounting for fluctuations in gravel content. The numerical findings indicate that gravel content markedly affects the displacement and deformation characteristics of the landslide. As the gravel concentration rises, landslide displacement progressively diminishes, with elevated gravel content enhancing the structural integrity of the landslide mass. This study underscores gravel content as a pivotal element in landslide deformation and reinforces its significance in assessing landslide stability and forecasting. Full article

Current research lacks an in-depth exploration of ensemble learning and factor engineering applications in regard to landslide susceptibility modeling. In the Three Gorges Reservoir area of China, a region prone to frequent landslides that endanger lives and infrastructure, this study advances landslide susceptibility prediction by integrating ensemble learning with systematic factor engineering. Four homogeneous ensemble models (random forest, XGBoost, LightGBM, and CatBoost) and two heterogeneous ensembles (bagging and stacking) were implemented to evaluate 14 influencing factors. The key results demonstrate the Digital Elevation Model (DEM) as the dominant factor, while the stacking ensemble achieved superior performance (AUC = 0.876), outperforming single models by 4.4%. Iterative factor elimination and hyperparameter tuning increased the high-susceptibility zones in the stacking predictions to 42.54% and enhanced XGBoost’s low-susceptibility classification accuracy from 12.96% to 13.57%. The optimized models were used to generate a high-resolution landslide susceptibility map, identifying 23.8% of the northern and central regions as high-susceptibility areas, compared to only 9.3% as eastern and southern low-susceptibility zones. This methodology improved the prediction accuracy by 12–18% in comparison to a single model, providing actionable insights for landslide risk mitigation. Full article

The article presents an analysis of the complex mechanism of abrasion of shorelines built of non-lithified sediments as a result of rising water levels in the reservoir, along with its quantitative assessment. It allows forecasting the actual risks of coastal areas intendent for urbanization with similar morphology and geological structure. The task of the article is also to point out that for proper assessment of abrasion it is necessary to take into account the greater complexity of the mechanism in which abrasion is the result of co-occurring processes of erosion and landslides. During the analysis, the classic Kachugin method of abrasion assessment was combined with an analysis of the stability of the abraded slope, taking into account the circular slip surface (Bishop and Morgenster–Price methods) and the breaking slip surface (Sarma method). This approach required the assessment of the geotechnical properties of the soil using, among other things, advanced in situ methods such as static sounding. The results indicate that the cliff edge is in limit equilibrium or even in danger of immediate landslide. At the same time, it was possible to determine the horizontal extent of a single landslide at 1.2 to 5.8 m. In the specific cases of reservoir filling, the consideration of the simultaneous action of both failure mechanisms definitely worsens the prediction of shoreline sustainability and indicates the need to restrict construction development in the coastal zone. Full article

The coupling of water level fluctuations and heavy rainfall in the Three Gorges reservoir area poses a significant threat to the stability of bank slopes, especially in landslide areas with complex geological conditions. In this study, the Huasushu slope in Fengjie County, Chongqing, was taken as the research object and, based on a field investigation and monitoring data, two- and three-dimensional numerical models were constructed to analyze the response mechanism of the slope under the combined effects of different reservoir water level decreases and rainfall. In addition, the safety coefficients under each working condition were calculated using the Morgenstern–Price method. The results show that it is difficult to trigger significant deformation with a single water level drop or rainfall. However, when the reservoir water level drops more than 10 m within a short period of time and is superimposed with strong rainfall, the landslide body is prone to plastic zone extension and significant displacement, showing typical strain localization characteristics. The three-dimensional model further reveals the spatial distribution characteristics of the landslide deformation area, which helps to accurately identify potential destabilization locations. The research results provide theoretical support for the construction of early warning systems for reservoir bank slopes and have reference value for the development of disaster mitigation engineering measures based on the coupling mechanism of rainwater and reservoir water. Full article