Description of a complex, rainfall-induced landslide within a multi-stage three-dimensional model

Tran, Thanh-Tung; Alvioli, Massimiliano; Hoang, Viet Hung

doi:10.1007/s11069-021-05020-0

Cited by 16 publications

(9 citation statements)

References 37 publications

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“…The Mohr circle for the stress of the soil became smaller, and the shear strength decreased. The pore water pressure caused the stress state of the points on the sliding surface of the end wall to gradually change from a stable state to a critical stable state (Fan et al, 2017;Tran et al, 2021).…”

Section: ) Influence Of the Fault Fracture Zone On The End-wall Stabi...mentioning

confidence: 99%

“…Numerous studies have demonstrated that most retrogressive landslides occur in clay-rich strata, especially during periods of rainfall (Jaboyedoff et al, 2009;Locat et al, 2011;Zhang et al, 2018). Examples of retrogressive landslides include the Hongao dump landslide in Shenzhen, China (Yin et al, 2016), the Chaoyang open-pit coal mine in China (Figure 2D), and the Sapa landslide in Vietnam (Tran et al, 2022). In China, thrust load-caused landslides primarily occur in the Three Gorges Reservoir area, the Sichuan Basin (Song et al, 2021a), and the Inner Mongolia open-pit coal mines.…”

Section: Introductionmentioning

confidence: 99%

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Landslide mechanism and stability of an open-pit slope: The Manglai open-pit coal mine

Chen

Shu²,

Liu³

et al. 2023

Front. Earth Sci.

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A clear understanding of landslide mechanisms and stability analyses is of great significance for landslide monitoring, prediction, and control. A large-scale end wall landslide occurred and its area reached 47,752 m2 on August 7–20, 2020, in the Manglai open-pit coal mine, China. In this paper, the engineering geological survey, mechanical test, large-deformation finite element numerical method, limit equilibrium method and analytical formula are used to analyze how the groundwater level rise caused this end wall failure and landslide. The engineering geological conditions, hydrogeological conditions, landslide activity signs and physical and mechanical parameter calibration of the sliding mass are investigated in detail and tested. Three-dimensional and two-dimensional numerical models of slopes are established, and an analytical formula calculation method to calculate the factor of safety (FoS) is proposed when sliding cracks are located on the top and toe of a slope. The results indicate that the fault fracture zone, soft strata, continuous heavy rainfall, and groundwater were the main contributors to this landslide. The three-dimensional numerical calculation results are consistent with the deformation and failure process of the slope observed in the field, which shows a retrogressive progressive failure mode. The calculation results of SLOPE/W and the analytical formula are consistent; i.e., when the groundwater level rises to +950, the stability of the end wall reaches the critical stability state. With the passage of time, the creep of the sliding surface accelerates until the entire end wall landslide.

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Section: ) Influence Of the Fault Fracture Zone On The End-wall Stabi...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Landslide mechanism and stability of an open-pit slope: The Manglai open-pit coal mine

Chen

Shu²,

Liu³

et al. 2023

Front. Earth Sci.

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“…The factors affecting landslides are often very complex and can be divided into internal factors and external factors according to their sources [50][51][52]. Adverse environmental geological conditions, including slope landforms, thick and loose soil layers, highly weathered lithology, and large-scale free surfaces formed via engineering excavation, are the main internal factors for the occurrence and development of landslides [53].…”

Section: Analysis Of Monitoring Data and Triggering Factorsmentioning

confidence: 99%

A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network

Lin,

Yang,

Huang

et al. 2023

Water

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Influenced by autochthonous geological conditions and external environmental changes, the evolution of landslides is mostly nonlinear. This article proposes a combined neural network prediction model that combines a temporal convolutional neural network (TCN) and a bidirectional long short-term memory neural network (BiLSTM) to address the shortcomings of traditional recurrent neural networks in predicting displacement-fluctuation-type landslides. Based on the idea of time series decomposition, the improved complete ensemble empirical mode decomposition with an adaptive noise method (ICEEMDAN) was used to decompose displacement time series data into trend and fluctuation terms. Trend displacement is mainly influenced by the internal geological conditions of a landslide, and polynomial fitting is used to determine the future trend displacement; The displacement of the fluctuation term is mainly influenced by the external environment of landslides. This article selects three types of landslide-influencing factors: rainfall, groundwater level elevation, and the historical displacement of landslides. It uses a combination of gray correlation (GRG) and mutual information (MIC) correlation modules for feature screening. Then, TCN is used to extract landslide characteristic factors, and BiLSTM captures the relationship between features and displacement to achieve the prediction of wave term displacement. Finally, the trend term and fluctuation term displacement prediction values are reconstructed to obtain the total displacement prediction value. The results indicate that the ICEEMDAN–TCN–BiLSTM model proposed in this article can accurately predict landslide displacement and has high engineering application value, which is helpful for planning and constructing landslide disaster prevention projects.

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“…This evaluation includes an examination of numerous elements that might contribute to landslides, such as land use, rainfall, slope, rainfall, and geology (Pourghasemi et al, 2018). The involvement of fluvial processes and the hydro-geomorphic characteristics they generate is one crucial component that might contribute to the danger of landslides (Tran et al, 2022). These water-moving activities can change the terrain in ways that raise the danger of landslides.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for high-resolution landslide susceptibility mapping: case study in Inje County, South Korea

Le,

Eu,

Choi

et al. 2023

Front. Earth Sci.

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Landslides are a major natural hazard that can significantly damage infrastructure and cause loss of life. In South Korea, the current landslide susceptibility mapping (LSM) approach is mainly based on statistical techniques (logistic regression (LR) analysis). According to previous studies, this method has achieved an accuracy of approximately 75.2%. In this paper, we expand upon this traditional approach by comparing the performance of six machine learning (ML) algorithms for LSM in Inje County, South Korea. The study employed a combination of geographical data gathered from 2005 to 2019 to train and evaluate six algorithms, including LR, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGB). The effectiveness of these models was measured by various criteria, such as the percentage of correct classification (PCC) score, F1 score, and Kappa score. The results demonstrated that the PCC and F1 scores of the six models fell between [0.869–0.941] and [0.857–0.940], respectively. RF and XGB had the highest PCC and F1 scores of 0.939 and 0.941, respectively. This study indicates that ML can be a valuable technique for high-resolution LSM in South Korea instead of the current approach.

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Description of a complex, rainfall-induced landslide within a multi-stage three-dimensional model

Cited by 16 publications

References 37 publications

Landslide mechanism and stability of an open-pit slope: The Manglai open-pit coal mine

Landslide mechanism and stability of an open-pit slope: The Manglai open-pit coal mine

A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network

Machine learning for high-resolution landslide susceptibility mapping: case study in Inje County, South Korea

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