Stability characteristics of shallow landslide triggered by rainfall

Chinkulkijniwat, Avirut; Tirametatiparat, Taworn; Supotayan, Chanathip; Yubonchit, Somjai; Horpibulsuk, Suksun; Salee, Rattana; Voottipruex, Panich

doi:10.1007/s11629-019-5523-7

Cited by 24 publications

(9 citation statements)

References 22 publications

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“…Most landslides occur in wet, partially saturated soils due to prolonged or intense rainfall, snowmelt, or seismic activity (Leonarduzzi et al, 2017;Schuster and Wieczorek, 2018). An increasing water content in the soil induces a reduction of soil shear strength leading to the failure of soil material within a shear band, usually situated at the interface of below-ground discontinuities such as between soil and bedrock (Catani et al, 2010;Zhang et al, 2017;Xiao et al, 2023) or between different soil horizons (Li et al, 2013;Ali et al, 2014;Ran et al, 2018;Chinkulkijniwat et al, 2019). This boundary defines the failure plane of shallow landslides and is as such implemented in physically-based models (Ran et al, 2018).…”

Section: Influences On Landslide Failure Thicknessmentioning

confidence: 99%

Predicting the thickness of shallow landslides in Switzerland using machine learning

Schaller,

Dorren,

Schwarz

et al. 2024

Preprint

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Abstract. Landslide thickness is a key parameter in various types of models used to simulate landslide susceptibility. In this study, we developed a model providing improved predictions of potential shallow landslide thickness in Switzerland. We tested three machine learning models based on random forests, generalized additive model, and linear regression and compared the results to three existing models that link soil thickness to slope and elevation. The models were calibrated using data from two field inventories in Switzerland ("HMDB" with 709 records and "KtBE" with 517 records). We explored 37 different covariates including metrics on terrain, geomorphology, vegetation height, and lithology at three different cell sizes. To train the machine learning models, 21 variables were chosen based on the variable importance derived from random forest models and expert judgement. Our results show that the machine learning models consistently outperformed the existing models by reducing the mean absolute error by at least 17 %. The random forests models produced a mean absolute error of 0.25 m for the HMDB and 0.19 m for the KtBE data. Models based on machine learning substantially improve the prediction of landslide thickness, offering refined input for enhancing the performance of slope stability simulations.

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Section: Influences On Landslide Failure Thicknessmentioning

confidence: 99%

Predicting the thickness of shallow landslides in Switzerland using machine learning

Schaller,

Dorren,

Schwarz

et al. 2024

Preprint

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“…After understanding the motion parameters of raindrops, we also need to analyze the influence of raindrops on sandcastles [26].…”

Section: Model Establishing and Solvingmentioning

confidence: 99%

Optimal Simulation of Sandcastle Life in Dynamic Environment Based on Stability Principle

Yang

Jiang

Tang

et al. 2020

Discrete Dynamics in Nature and Society

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This paper mainly studies to explore a three-dimensional geometric model including three modules of a sandcastle foundation with optimal stability. Firstly, based on the knowledge of streamline structure, structural mechanics, and fluid mechanics, the most stable sand pile foundation model under the action of tidal current and wave is established. Secondly, by limiting the degree of allowable aggregation, the discrete global optimization algorithm based on the continuous descent method is adopted to find out the optimal water-sand ratio. Finally, we apply the above results to verify the reliability of the optimal model by comprehensively considering the influence of rainfall factors on sandcastles.

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“…The formation and evolution of landslides in the TGRA are influenced by various dynamic and internal factors. Among these are regional geological background, reservoir water level fluctuations and rainfall 3,4 , as well as slope structure 5 , sliding surface morphology 6 , wading degree and permeability characteristics 7 . Reasonable evaluation of landslide stability is crucial for the prevention and control of geological disasters.…”

Section: Introductionmentioning

confidence: 99%

Analysis of deformation mechanism of rainfall-induced landslide in the Three Gorges Reservoir Area: Piansongshu Landslide

Wang

Zou

et al. 2023

Preprint

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The Three Gorges Reservoir Area (TGRA) has unique geological conditions that result in the formation of landslides. These landslides are prone to instability when exposed to external factors like rainfall. This study focuses on the Piansongshu landslide in the TGRA and aims to analyze the internal deformation mechanism of rainfall-induced landslides, providing valuable insights for landslide prevention and control. The study begins with field geological investigations to analyze the macroscopic deformation signs and deformation mechanisms of the landslide. GeoStudio numerical simulation software is employed to assess landslide stability, considering changes in internal seepage fields and stability under varying rainfall conditions. The results indicate that with continuous rainfall, a transient saturation zone appears on the slope's surface and extends downwards. The accumulation front edge's infiltration line protrudes towards the surface in areas with greater deformation. The stability coefficient of the landslide's secondary shear surface exhibits more significant variation compared to the overall sliding surface. Higher rainfall intensity and duration lead to a greater decrease in the stability coefficient. Different types of rainfall also impact stability differently. Over time, as rainfall infiltrates, the slope safety coefficient gradually decreases. Even after rainfall ceases, the stability coefficient continues to decline, requiring time for recovery until it reaches a stable state.

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Stability characteristics of shallow landslide triggered by rainfall

Cited by 24 publications

References 22 publications

Predicting the thickness of shallow landslides in Switzerland using machine learning

Predicting the thickness of shallow landslides in Switzerland using machine learning

Optimal Simulation of Sandcastle Life in Dynamic Environment Based on Stability Principle

Analysis of deformation mechanism of rainfall-induced landslide in the Three Gorges Reservoir Area: Piansongshu Landslide

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