Quantitative Assessment of Soil Landslide Impact Risk Based on the GA-BP Improved Random Material Point Method

Wang Zongyao; Zhou Xiaomin; Wu Rui; Sun Zheng

doi:10.21656/1000-0887.460137

Volume 47 Issue 9

Sep. 2026

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Article Navigation > Applied Mathematics and Mechanics > 2026 > 47(9): 1144-1158

Wang Zongyao, Zhou Xiaomin, Wu Rui, Sun Zheng. Quantitative Assessment of Soil Landslide Impact Risk Based on the GA-BP Improved Random Material Point Method[J]. Applied Mathematics and Mechanics, 2026, 47(9): 1144-1158. doi: 10.21656/1000-0887.460137

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Quantitative Assessment of Soil Landslide Impact Risk Based on the GA-BP Improved Random Material Point Method

doi: 10.21656/1000-0887.460137

Wang Zongyao^{1, 2
,},
Zhou Xiaomin^{1, 2},
Wu Rui^{1, 2},
Sun Zheng^{1, 2
,
,}

1.
School of Civil Engineering and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P. R. China
2.
Jiangxi Provincial Key Laboratory of River Source Water Ecological Protection, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P. R. China

Received Date: 2025-07-30
Rev Recd Date: 2026-07-18
Publish Date: 2026-09-01

Abstract

Abstract

The quantitative assessment of landslide risk is an important means to achieve landslide risk management. However, the existing assessment methods still have certain limitations in accuracy and applicability, and it is difficult to effectively characterize the uncertainty characteristics of the landslide process. The accuracy of the Monte Carlo simulation method in slope stability analysis has been widely recognized by the industry, but its application in engineering practice is greatly limited due to its high computational cost and long computational time. In addition, soil parameters show significant spatial variability under the long-term action of the geological environment, and traditional methods are difficult to describe this characteristic. To more realistically characterize the variation characteristics of soil in space, the spatial variability was considered and the stochastic material point method (SMPM) was developed based on the random field theory. Based on the 4 quantitative evaluation indicators of the maximum sliding kinetic energy, the maximum sliding pressure, the half bandwidth and the starting point, the risk of the entire soil landslide process was quantitatively assessed. At the same time, to further improve the computational efficiency and prediction accuracy, the genetic algorithm was combined with the BP neural network to construct a GA-BP neural network model to be integrated with the MC-SMPM method. The model uses the BP neural network to estimate the nonlinear relationship between soil parameters and quantitative risk assessment indicators, and optimizes the network structure through genetic algorithms, including key parameters such as the number of hidden layer neurons and the number of training times, to adapt to the characteristics of soil parameters, thereby improving the computational efficiency of the quantitative landslide risk assessment. The research results show that, the BP neural network model based on the GA optimization has significant improvements in overall prediction accuracy and stability, and can effectively promote the efficiency of quantitative landslide risk assessment and provide an efficient and reliable numerical calculation method for engineering applications.
- quantitative assessment of landslide risk,
- stochastic material point method,
- GA-BP model,
- random field theory

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References(30)

References

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