应用数学和力学2026，Vol.47Issue(9)：1144-1158,15.DOI:10.21656/1000-0887.460137
基于GA-BP改进随机物质点法的土质滑坡冲击风险定量评估
Quantitative Assessment of Soil Landslide Impact Risk Based on the GA-BP Improved Random Material Point Method
摘要
Abstract
The quantitative assessment of landslide risk is an important means to achieve landslide risk manage-ment.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 charac-terize the variation characteristics of soil in space,the spatial variability was considered and the stochastic ma-terial point method(SMPM)was developed based on the random field theory.Based on the 4 quantitative eval-uation 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 quan-titative 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 effi-ciency of quantitative landslide risk assessment and provide an efficient and reliable numerical calculation meth-od for engineering applications.关键词
滑坡风险定量评估/随机物质点法/GA-BP模型/随机场论Key words
quantitative assessment of landslide risk/stochastic material point method/GA-BP model/random field theory分类
建筑与水利引用本文复制引用
王宗耀,周晓敏,吴锐,孙政..基于GA-BP改进随机物质点法的土质滑坡冲击风险定量评估[J].应用数学和力学,2026,47(9):1144-1158,15.基金项目
国家自然科学基金地区科学基金项目(12262013) （12262013）
江西省自然科学基金杰出青年基金(20232ACB211005) （20232ACB211005）
