A three-dimensional two-level gradient smoothing meshfree method for rainfall induced landslide simulations

Wang, Dongdong; Wang, Jiarui; Wu, Jianhua; Deng, Junjun; Sun, Mingyue

doi:10.1007/s11709-018-0467-5

Cited by 19 publications

(9 citation statements)

References 58 publications

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“…Cremonesi et al (2017) presents a Lagrangian finite element (FE) technique with a basal slip model used for three-dimensional simulations of landslides. In contrast to the standard FE method for elastoplastic flows (Crosta et al 2003), different alternatives have been developed in order to overcome the otherwise unfeasible computational effort, such as depth averaging (Quecedo et al 2004) for landslides in which the depth is much smaller than the length, or mesh-free methods (Wang et al 2019) for large deformation modeling.…”

Section: Landslidesmentioning

confidence: 99%

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

et al. 2021

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Recent advances in satellite technologies, statistical and mathematical models, and computational resources have paved the way for operational use of satellite data in monitoring and forecasting natural hazards. We present a review of the use of satellite data for Earth observation in the context of geohazards preventive monitoring and disaster evaluation and assessment. We describe the techniques exploited to extract ground displacement information from satellite radar sensor images and the applicability of such data to the study of natural hazards such as landslides, earthquakes, volcanic activity, and ground subsidence. In this context, statistical techniques, ranging from time series analysis to spatial statistics, as well as continuum or discrete physics-based models, adopting deterministic or stochastic approaches, are irreplaceable tools for modeling and simulating natural hazards scenarios from a mathematical perspective. In addition to this, the huge amount of data collected nowadays and the complexity of the models and methods needed for an effective analysis set new computational challenges. The synergy among statistical methods, mathematical models, and optimized software, enriched with the assimilation of satellite data, is essential for building predictive and timely monitoring models for risk analysis.

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Section: Landslidesmentioning

confidence: 99%

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

et al. 2021

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“…Some scholars have done a lot of meaningful work. For example, Wang introduced a meshfree method for simulating rainfall-induced landslides, which employs a two-level gradient smoothing approach in a three-dimensional setting (Wang et al, 2014(Wang et al, , 2019. Guan proposed a meshfree Semi-Lagrangian reproducing kernel (RK) formulation to simulate earth-moving operations (Guan et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Designing an efficient smoothed point interpolation method for modeling slope deformation

Qin

Mei

2023

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PurposeIn this paper, the smoothed point interpolation method (SPIM) is used to model the slope deformation. However, the computational efficiency of SPIM is not satisfying when modeling the large-scale nonlinear deformation problems of geological bodies.Design/methodology/approachIn this paper, the SPIM is used to model the slope deformation. However, the computational efficiency of SPIM is not satisfying when modeling the large-scale nonlinear deformation problems of geological bodies.FindingsA simple slope model with different mesh sizes is used to verify the performance of the efficient face-based SPIM. The first accelerating strategy greatly enhances the computational efficiency of solving the large-scale slope deformation. The second accelerating strategy effectively improves the convergence of nonlinear behavior that occurred in the slope deformation.Originality/valueThe designed efficient face-based SPIM can enhance the computational efficiency when analyzing large-scale nonlinear slope deformation problems, which can help to predict and prevent potential geological hazards.

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“…Based upon SCNI, Wang et al. (2011, 2014, 2019) developed a three dimensional two-level gradient smoothing large deformation Lagrangian meshfree framework for rainfall induced slope failure simulation. To more effectively deal with the extreme deformation in geomaterials, the semi-Lagrangian meshfree formulation has been elaborated by Guan et al.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned earlier, although the strength of the particle-based meshfree methods in modeling large deformation damage and failure problems has been extensively demonstrated (Baek et al., 2022; Chen et al., 1996; Jiang et al., 2022; Wang et al., 2019; Wu et al., 2016), the relatively low computational efficiency actually prevents their practical applications. The aim of this work is to develop an encoder-decoder model with embedded attention-mechanism to enable an efficient meshfree prediction of large deformation slope failure.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this work is to develop an encoder-decoder model with embedded attention-mechanism to enable an efficient meshfree prediction of large deformation slope failure. The model training data are produced by the two-level gradient smoothing Lagrangian meshfree formulation (Wang et al., 2019). Thanks to the discretization flexibility meshfree particles, it is trivial to match the meshfree particles and training samplings (Chen et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

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An encoder-decoder model with embedded attention-mechanism for efficient meshfree prediction of slope failure

Chen,

Wang,

Deng

et al. 2023

International Journal of Damage Mechanics

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The particle-based meshfree methods provide an effective means for large deformation simulation of the slope failure. Despite the advances of various efficient meshfree algorithmic developments, the computational efficiency still limits the application of meshfree methods for practical problems. This study aims at accelerating the meshfree prediction of the slope failure through introducing an encoder-decoder model, which is particularly enhanced by the attention-mechanism. The encoder-decoder model is designed to capture the long sequence character of meshfree slope failure analysis. The discretization flexibility of meshfree methods offers an easy match between the meshfree particles and machine learning samples and thus the resulting surrogate model for meshfree slope failure prediction has a quite wide applicability. In the meantime, the embedding of the attention-mechanism into the encoder-decoder neural network not only enables a significant reduction of the number of meshfree model parameters, but also maintains the key features of meshfree simulation and effectively alleviates the information dilution issue. It is shown that the proposed encoder-decoder model with embedded attention mechanism gives a more favorable prediction on the meshfree slope failure simulation in comparison to the general encoder-decoder formalism.

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A three-dimensional two-level gradient smoothing meshfree method for rainfall induced landslide simulations

Cited by 19 publications

References 58 publications

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

Designing an efficient smoothed point interpolation method for modeling slope deformation

An encoder-decoder model with embedded attention-mechanism for efficient meshfree prediction of slope failure

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