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

王宗耀; 周晓敏; 吴锐; 孙政

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

doi: 10.21656/1000-0887.460137

王宗耀^1,2,
周晓敏^1,2,
吴锐,
孙政^1,2, ,

1. 江西理工大学土木与测绘工程学院，江西赣州 341000;
2. 江西理工大学河流源头水生态保护江西省重点实验室，江西赣州 341000

基金项目:

国家自然科学基金地区科学基金项目(12262013)；江西省自然科学基金杰出青年基金(20232ACB211005)

详细信息

作者简介:
王宗耀（2000—），男，硕士生(E-mail: 6720230245@mail.jxust.edu.cn);孙政（1986—），男，副教授，博士，博士生导师(通信作者. E-mail: sunzheng@jxust.edu.cn).

通讯作者:
孙政（1986—），男，副教授，博士，博士生导师(通信作者. E-mail: sunzheng@jxust.edu.cn).

中图分类号: TU433
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出版历程
- 收稿日期: 2025-07-30
- 修回日期: 2026-07-18
- 网络出版日期: 2026-09-24
- 刊出日期: 2026-09-01

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

Wang Zongyao^1,2,
Zhou Xiaomin^1,2,
Wu Rui,
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

摘要

摘要: 滑坡风险定量评估是实现滑坡风险管理的重要手段.现有的评估方法在准确性和适用性方面仍存在一定局限，难以有效地表征滑坡过程中的不确定性特征.Monte-Carlo模拟方法在边坡稳定性分析中的精确度已经得到业界的广泛认同，但由于计算成本高，计算时间长，其在工程实际中的应用受到了较大的限制.此外，土体参数在地质环境长期作用下呈现显著的空间变异性，而传统方法难以描述这一特性.为更真实地表征土体在空间中所呈现的变异特征，本文考虑空间变异性，基于随机场理论，发展了随机物质点法(stochastic material point method，SMPM)；基于最大滑动动能、最大滑动压力、半带宽与起跳点四个定量评估指标，对土质滑坡全过程进行风险定量评估.同时，为进一步提高计算效率与预测精度，将遗传算法(genetic algorithm，GA)与BP神经网络结合，构建GA-BP神经网络模型，并将其与MC-SMPM方法相融合.该模型利用BP神经网络估计土体参数与风险定量评估指标之间的非线性关系，通过遗传算法优化网络的结构，包括隐藏层神经元数量和训练次数等关键参数，以适配土体参数特征，从而提升滑坡风险定量评估的计算效率.研究结果表明，基于GA优化的BP神经网络模型，在整体预测精度和稳定性方面均有显著提升，能够有效提高滑坡风险定量评估效率，为工程实际应用提供了一种高效、可靠的数值计算方法.
- 滑坡风险定量评估 /
- 随机物质点法 /
- GA-BP模型 /
- 随机场论
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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参考文献(30)

蒋水华, 杨建华, 姚池, 等. 考虑土体参数空间变异性边坡失稳风险定量评估[J]. 工程力学, 2018,35(1): 136-147.

(Jiang Shuihua, Yang Jianhua, Yao Chi, et al. Quantitative risk assessment of slope failure considering spatial variability of soil properties[J].Engineering Mechanics,2018,35(1): 136-147. (in Chinese))

[2]Tan X H, Wang X, Khoshnevisan S, et al. Seepage analysis of earth dams considering spatial variability of hydraulic parameters[J].Engineering Geology,2017,228: 260-269.

[3]陈朝晖, 雷坚, 黄景华, 等. 考虑参数空间变异性的边坡稳定可靠性有限元极限分析[J]. 岩土工程学报, 2018,40(6): 985-993. (Chen Zhaohui, Lei Jian, Huang Jinghua, et al. Finite element limit analysis of slope stability considering spatial variability of soil strengths[J].Chinese Journal of Geotechnical Engineering,2018,40(6): 985-993. (in Chinese))

[4]Yu X, Li Z, Wang Y, et al. Stress-deformation analysis of concrete anti-seepage structure in earth-rock dam on overburden considering spatial variability of geomechanical parameter[J].Probabilistic Engineering Mechanics,2025,79: 103725.

[5]Cheng H, Zhou J, Chen Z, et al. A comparative study of the seismic performances and failure mechanisms of slopes using dynamic centrifuge modeling[J].Journal of Earth Science,2021,32(5): 1166-1173.

[6]Li Y C, Pang R, Xu B. Dynamic reliability analysis of three-dimensional slopes considering the spatial variability in soil parameters[J].Structures,2023,56: 104977.

[7]Effati D D M B, Bahadori H, Daryani K E. Soil probabilistic slope stability analysis using stochastic finite difference method[M]//Geotechnical Safety and Risk V,IOS Press, 2015: 710-715.

[8]Li P, Shen W, Hou X K, et al. Numerical simulation of the propagation process of a rapid flow-like landslide considering bed entrainment: a case study[J].Engineering Geology,2019,263: 105287.

[9]Sun Z,Gan Y, Tao J, et al. An improved quadrature scheme in B-spline material point method for large-deformation problem analysis[J].Engineering Analysis With Boundary Elements,2022,138: 301-318.

[10]Sulsky D, Chen Z, Schreyer H L. A particle method for history-dependent materials[J].Computer Methods in Applied Mechanics and Engineering,1994,118(1/2): 179-196.

[11]王长生, 于传泽, 张向奎. 基于对流粒子域插值物质点法的壳结构分析[J]. 应用数学和力学, 2024,45(9): 1147-1156. (Wang Changsheng, Yu Chuanze, Zhang Xiangkui. Shell structure analysis based on the convected particle domain interpolation[J].Applied Mathematics and Mechanics,2024,45(9): 1147-1156. (in Chinese))

[12]韩方建, 库启贤, 于海军, 等. 基于物质点法的岩石单孔爆破数值模拟研究[J]. 应用数学和力学, 2025,46(10): 1320-1328. (Han Fangjian, Ku Qixian, Yu Haijun, et al. Numerical simulation of single hole blasting of rock based on the material point method[J].Applied Mathematics and Mechanics,2025,46(10): 1320-1328. (in Chinese))

[13]Bandara S, Ferrari A, Laloui L. Modelling landslides in unsaturated slopes subjected to rainfall infiltration using material point method[J].International Journal for Numerical and Analytical Methods in Geomechanics,2016,40(9): 1358-1380.

[14]Andersen S, Andersen L. Modelling of landslides with the material-point method[J].Computational Geosciences,2010,14(1): 137-147.

[15]孙玉进, 宋二祥. 大位移滑坡形态的物质点法模拟[J]. 岩土工程学报, 2015,37(7): 1218-1225. (Sun Yujin, Song Erxiang. Simulation of large-displacement landslide by material point method[J].Chinese Journal of Geotechnical Engineering,2015,37(7): 1218-1225. (in Chinese))

[16]张巍, 史卜涛, 施斌, 等. 土质滑坡运动全过程物质点法模拟及其应用[J]. 工程地质学报, 2017,25(3): 815-823. (Zhang Wei, Shi Butao, Shi Bin, et al. Material point method for run-out process simulation of soil landslides and application[J].Journal of Engineering Geology,2017,25(3): 815-823. (in Chinese))

[17]刘磊磊, 梁昌奇, 徐蒙, 等. 考虑参数旋转各向异性空间变异性的边坡大变形概率分析[J]. 地球科学, 2023,48(5): 1836-1852. (Liu Leilei, Liang Changqi, Xu Meng, et al. Probabilistic analysis of large slope deformation considering soil spatial variability with rotated anisotropy[J].Earth Science,2023,48(5): 1836-1852. (in Chinese))

[18]Wang B, Hicks M A, Vardon P J. Slope failure analysis using the random material point method[J].Géotechnique Letters,2016,6(2): 113-118.〖JP〗

[19]Liu X, Wang Y, Li D Q. Investigation of slope failure mode evolution during large deformation in spatially variable soils by random limit equilibrium and material point methods[J].Computers and Geotechnics,2019,111: 301-312.

[20]吴锐, 孙政, 岳臣龙, 等. 基于蒙特卡洛物质点法的土质滑坡全过程风险定量评估[J]. 力学与实践, 2023,45(4): 805-817. (Wu Rui, Sun Zheng, Yue Chenlong, et al. Quantitative assessment of the whole process risk of soil landslide based on Monte Carlo material point method[J].Mechanics in Engineering,2023,45(4): 805-817. (in Chinese))

[21]Li J, Zhu D, Li C. Comparative analysis of BPNN, SVR, LSTM, Random Forest, and LSTM-SVR for conditional simulation of non-Gaussian measured fluctuating wind pressures[J].Mechanical Systems and Signal Processing,2022,178: 109285.

[22]Zhang Y G, Tang J, Liao R P, et al. Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction[J].Stochastic Environmental Research and Risk Assessment,2021,35(6): 1273-1291.

[23]Bredael D, Vanhoucke M. A genetic algorithm with resource buffers for the resource-constrained multi-project scheduling problem[J].European Journal of Operational Research,2024,315(1): 19-34.

[24]Han Z, Huang Z. An interpolation method for Karhunen-Loève expansion of random field discretization[J].Computer Modeling in Engineering & Sciences,2024,138(1): 245-272.

[25]张继周, 缪林昌. 基于随机场理论的地基概率沉降分析[J]. 岩土工程学报, 2010,32(7): 1059-1064. (Zhang Jizhou, Miao Linchang. Probabilistic foundation settlement based on random field theory[J].Chinese Journal of Geotechnical Engineering,2010,32(7): 1059-1064. (in Chinese))

[26]王涛, 周国庆, 阴琪翔, 等. 岩土参数随机场离散的三角形单元局部平均法[J]. 岩土力学, 2014,35(5): 1482-1488. (Wang Tao, Zhou Guoqing, Yin Qixiang, et al. Local average method of triangular elements for discretization of random field of geotechnical parameters[J].Rock and Soil Mechanics,2014,35(5): 1482-1488. (in Chinese))

[27]Troncone A, Pugliese L, Conte E. Analysis of an excavation-induced landslide in stiff clay using the material point method[J].Engineering Geology,2022,296: 106479.

[28]Phoon K K, Kulhawy F H. Characterization of geotechnical variability[J].Canadian Geotechnical Journal,1999,36(4): 612-624.

[29]Phoon K K, Kulhawy F H. Evaluation of geotechnical property variability[J].Canadian Geotechnical Journal,1999,36(4): 625-639.

[30]柏晗, 杨耘, 崔琴芳, 等. 基于GA-XGBoost模型的GF-5卫星影像土壤重金属含量反演研究[J]. 激光与光电子学进展, 2022,59(12): 515-524. (Bai Han, Yang Yun, Cui Qinfang, et al. Retrieval of heavy metal content in soil using GF-5 satellite images based on GA-XGBoost model[J].Laser & Optoelectronics Progress,2022,59(12): 515-524. (in Chinese))

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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

doi: 10.21656/1000-0887.460137

作者简介:
王宗耀（2000—），男，硕士生(E-mail: 6720230245@mail.jxust.edu.cn);孙政（1986—），男，副教授，博士，博士生导师(通信作者. E-mail: sunzheng@jxust.edu.cn).

通讯作者:
孙政（1986—），男，副教授，博士，博士生导师(通信作者. E-mail: sunzheng@jxust.edu.cn).

计量

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

计量

目录

留言板

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

doi: 10.21656/1000-0887.460137

作者简介: 王宗耀（2000—），男，硕士生(E-mail: 6720230245@mail.jxust.edu.cn);孙政（1986—），男，副教授，博士，博士生导师(通信作者. E-mail: sunzheng@jxust.edu.cn).

通讯作者: 孙政（1986—），男，副教授，博士，博士生导师(通信作者. E-mail: sunzheng@jxust.edu.cn).

计量

出版历程

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

计量

出版历程

目录

作者简介:
王宗耀（2000—），男，硕士生(E-mail: 6720230245@mail.jxust.edu.cn);孙政（1986—），男，副教授，博士，博士生导师(通信作者. E-mail: sunzheng@jxust.edu.cn).

通讯作者:
孙政（1986—），男，副教授，博士，博士生导师(通信作者. E-mail: sunzheng@jxust.edu.cn).