Prediction of shallow landslide by surficial stability analysis considering rainfall infiltration

Cho, Sung Eun

doi:10.1016/j.enggeo.2017.10.018

Cited by 68 publications

(24 citation statements)

References 25 publications

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“…where σ represents the stress tensor in 2D space, γ(θ) represents the unit weight of the slope material related to water content and b is the unit vector of body forces with two components. The field of soil suction stress and effective stress can be estimated using Equations (5) and 7, respectively, with the solutions of the field of soil saturation, soil suction and total stress. The LFS method can then be used to calculate the soil stability field.…”

Section: Coupled Hydromechanical Frameworkmentioning

confidence: 99%

“…Studies on landslides, which occur in many parts of the world, have found that slope stability is influenced by not only internal factors, such as topography, geological structure of the slope and groundwater conditions but also external factors, such as precipitation, earthquakes and human activity [1][2][3][4][5]. Rainwater infiltration is the most common cause of landslides [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the Effect of Soil Hydraulic Conductivity Anisotropy on Slope Stability Using a Coupled Hydromechanical Framework

Yeh

Tsai

2018

Water

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In studies on the effect of rainfall on slope stability, soil hydraulic conductivity is usually assumed to be isotropic to simplify the analysis. In the present study, a coupled hydromechanical framework based on transient seepage analysis and slope stability analysis is used to investigate the effects of hydraulic conductivity anisotropy on rainfall infiltration and slope safety at various slope locations (the top of the slope, the slope itself and the toe of the slope). The results show that when the vertical hydraulic conductivity (K y) is constant, the horizontal hydraulic conductivity (K x) increases (i.e., anisotropy increases). This occurs because rainfall tends to infiltrate into the interior of the slope, resulting in the soil on top of the slope and on the slope itself being easily influenced by rainfall, leading to soil instability. The change of rainfall infiltration at the slope itself is the most significant. When the anisotropic ratio K r (=K x /K y) increased from 1 to 100, the depth of the wetting zones for loam, silt and clay slopes increased by 23.3%, 33.3% and 50%, respectively. However, increased K r led to a slower infiltration rate in the vertical direction at the toe of the slope. Compared to the results for K r = 1 and for K r = 100, the thickness of the wetting zones at the toe of loam and silt slopes decreased by 23.3% and 30.0%, respectively. For the clay slope, K r changes did not significantly affect the wetting zones because of poor permeability. The results of this study suggest that the effect of soil hydraulic conductivity anisotropy should be considered when estimating slope stability to better understand the effect of rainfall on slopes.

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Section: Coupled Hydromechanical Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing the Effect of Soil Hydraulic Conductivity Anisotropy on Slope Stability Using a Coupled Hydromechanical Framework

Yeh

Tsai

2018

Water

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“…Shallow landslides are one of the most common and frequent geo-disasters that occur in mountainous regions [1]. In most areas of Korea, where approximately 63% of the territory consists of mountainous regions, soil layers are generally less than 2-3 m in thickness with underlying bedrock [2].…”

Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions

2020

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Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving the factor selection method and various non-linear activation functions. A total of 151 historical landslide events and 20 predisposing factors consisting of Geographic Information System (GIS)-based morphological, hydrological, geological, and land cover datasets were constructed with a resolution of 5 x 5 m. The collected datasets were applied to information gain ratio analysis to confirm the predictive power and multicollinearity diagnosis to ensure the correlation of independence among the landslide predisposing factors. The best 11 predisposing factors that were selected in this study were randomly divided into a 70:30 ratio for training and validation datasets, which were used to produce ANN-based landslide susceptibility models. The ANN model used in this study had a multi-layer perceptron (MLP) structure consisting of an input layer, one hidden layer, and an output layer. In the output layer, the logistic sigmoid function was used to represent the result value within the range of 0 to 1, and six non-linear activation functions were used for the hidden layer. The performance of the landslide susceptibility models was evaluated using the receiver operating characteristic curve, Kappa index, and five statistical indices (sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) with the training dataset. In addition, the landslide susceptibility models were validated using the aforementioned measures with the validation dataset and were compared using the Friedman test to check the significant differences among the six developed models. The optimal number of neurons was determined based on the aforementioned performance evaluation and validation results. Overall, the model with the best performance was the MLP model with the logistic sigmoid activation function in the output layer and the hyperbolic tangent sigmoid activation function with five neurons in the hidden layer. The validation results of the best model showed a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, PPV of 79.17%, NPV of 81.82%, a Kappa index of 0.609, and AUC of 0.879. The results of this study highlight the effectiveness of selecting an optimal MLP model structure for shallow landslide susceptibility mapping using an appropriate predisposing factor section method.

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“…The rain that infiltrates into the soil increases the water content of the soil and the water pressure of the pores. When the pore water pressure of soil increases, its effective stress decreases, resulting in a decrease in the shear strength of the soil and a decrease in slope stability [10][11][12]. Therefore, rainfall seepage is considered to be a factor that causes slope damage.…”

Section: Introductionmentioning

confidence: 99%

Effect of Variations in Long-Duration Rainfall Intensity on Unsaturated Slope Stability

Yeh

Tsai

2018

Water

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In recent years, many scientific methods have been used to prove that the Earth's climate is changing. Climate change can affect rainfall patterns, which can in turn affect slope safety. Therefore, this study analyzed the effects of climate change on rainfall patterns from the perspective of rainfall intensity. This analysis was combined with numerical model analysis to examine the rainfall patterns of the Zengwen reservoir catchment area and its effects on slope stability. In this study, the Mann-Kendall test and the Theil-Sen estimator were used to analyze the rainfall records of rainfall stations at Da-Dong-Shan, Ma-To-Shan, and San-Jiao-Nan-Shan. The rainfall intensity of the Zengwen reservoir catchment area showed an increasing trend from 1990-2016. In addition, the analysis results of rainfall intensity trends were used for qualitative analysis of seepage and slope stability. The trend analysis result showed that in the future, from 2017-2100, if the amount of rainfall per hour continues to rise at about 0.1 mm per year, the amount of seepage will increase at the slope surface boundary and significantly change pore water pressure in the soil. As a result, the time of the occurrence of slope instability after the start of rainfall will decrease from 20 to 13 h, and the reduction in the safety coefficient will increase from 32 to 41%. Therefore, to decrease the effects of slope disasters on the safety of the Zengwen reservoir and its surrounding areas, changes in rainfall intensity trends should be considered for slope safety in this region. However, the results of trend analyses were weak and future research is needed using a wider range of precipitation data and detailed hydrological analysis to better predict rainfall pattern variations.

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Prediction of shallow landslide by surficial stability analysis considering rainfall infiltration

Cited by 68 publications

References 25 publications

Analyzing the Effect of Soil Hydraulic Conductivity Anisotropy on Slope Stability Using a Coupled Hydromechanical Framework

Analyzing the Effect of Soil Hydraulic Conductivity Anisotropy on Slope Stability Using a Coupled Hydromechanical Framework

Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions

Effect of Variations in Long-Duration Rainfall Intensity on Unsaturated Slope Stability

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