|Table of Contents|

Slope Stability Analysis Method Based on Machine Learning—Taking 618 Slopes in China as Examples(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2022年第06期
Page:
1083-1095
Research Field:
纪念刘国昌先生诞辰110周年专辑
Publishing date:

Info

Title:
Slope Stability Analysis Method Based on Machine Learning—Taking 618 Slopes in China as Examples
Author(s):
ZHANG Meng-han;  WEI Jin*;  BIAN Hai-ding
(School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China)
Keywords:
slope;  stability;  machine learning;  random forest;  intelligent evaluation;  characteristic parameter;  confusion matrix;  protection measure
PACS:
P642.2; TU457
DOI:
10.19814/j.jese.2022.09019
Abstract:
In order to quickly and accurately predict the slope stability, an intelligent evaluation method of slope stability based on machine learning was proposed. Based on the characteristics of 618 slopes in China, 6 typical slope parameters, including gravity(γ), cohesion(C), internal friction angle(φ), slope angle(β), slope height(H)and pore water pressure(P), were selected to establish the slope stability evaluation dataset. Slope stability prediction models were established using gradient boosting machine(GBM), support vector machine(SVM), artificial neural network(ANN)and random forest(RF)in the machine learning algorithm. The models were trained and learned using the training set. The model parameters were adjusted using the 5-fold cross validation and grid-search. The ability of the model to classify the slope stability was tested through the test set, and the optimal model was determined. The results show that by comparing the area under the receiver operating characteristic curve(AUC)and F1Score of GBM, SVM, ANN and RF algorithms, the AUC value and F1Score of RF algorithm are 0.969 and 0.904, respectively; RF algorithm has the best evaluation index and is more suitable for analyzing slope stability. Based on the different slope stability prediction models established by deleting different characteristic variables of RF algorithm, the characteristic parameters in descending order of sensitivity are γ, β, H, φ, P, C. According to the different sensitivity factors, the slope protection measures are proposed based on the sensitivity of the characteristic parameters.

References:

[1] 刘汉龙,马彦彬,仉文岗.大数据技术在地质灾害防治中的应用综述[J].防灾减灾工程学报,2021,41(4):710-722.
LIU Han-long,MA Yan-bin,ZHANG Wen-gang.Application of Big Data Techniques in Geological Disaster Analysis and Prevention:A Systematic Review [J].Journal of Disaster Prevention and Mitigation Engineering,2021,41(4):710-722.
[2] LAN H X,TIAN N M,LI L P,et al.Kinematic-based Landslide Risk Management for the Sichuan-Tibet Grid Interconnection Project(STGIP)in China[J].Engineering Geology,2022,308:106823.
[3] 罗元斌.边坡工程稳定性分析及处治技术研究[D].长沙:中南大学,2006.
LUO Yuan-bin.The Study on Analysing Stability and Treating Technique for Slope Engineering[D].Chang-sha:Central South University,2006.
[4] 彭 海.边坡工程处治技术分析研究及工程应用[D].长沙:中南大学,2011.
PENG Hai.Research of Analysis and Application on Slope Treatment Technical[D].Changsha:Central South University,2011.
[5] 兰恒星,彭建兵,祝艳波,等.黄河流域地质地表过程与重大灾害效应研究与展望[J].中国科学:地球科学,2022,52(2):199-221.
LAN Heng-xing,PENG Jian-bing,ZHU Yan-bo,et al.Research on Geological and Surfacial Processes and Major Disaster Effects in the Yellow River Basin[J].Science China:Earth Sciences,2022,52(2):199-221.
[6] 李红蕊.基于机器学习的边坡稳定性评价及位移预测研究[D].重庆:重庆大学,2021.
LI Hong-rui.Slope Stability Evaluation and Displacement Prediction Based on Machine Learning[D].Chongqing:Chongqing University,2021.
[7] 张 凯,张 科.基于LightGBM算法的边坡稳定性预测研究[J].中国安全科学学报,2022,32(7):113-120.
ZHANG Kai,ZHANG Ke.Prediction Study on Slope Stability Based on LightGBM Algorithm[J].China Safety Science Journal,2022,32(7):113-120.
[8] 王健伟,徐玉胜,李俊鑫.基于网格搜索支持向量机的边坡稳定性系数预测[J].铁道建筑,2019,59(5):94-97.
WANG Jian-wei,XU Yu-sheng,LI Jun-xin.Prediction of Slope Stability Coefficients Based on Grid Search Support Vector Machine[J].Railway Engineering,2019,59(5):94-97.
[9] 苏国韶,宋咏春,燕柳斌.高斯过程机器学习在边坡稳定性评价中的应用[J].岩土力学,2009,30(3):675-679.
SU Guo-shao,SONG Yong-chun,YAN Liu-bin.Application of Gaussian Process Machine Learning to Slope Stability Evaluation[J].Rock and Soil Mecha-nics,2009,30(3):675-679.
[10] KAYA A,MIDILLI U M.Slope Sability Evaluation and Monitoring of A Landslide:A Case Study from NE Turkey[J].Journal of Mountain Science,2020,17(11):2624-2635.
[11] LING Q,ZHANG Q,WEI Y M,et al.Slope Reliability Evaluation Based on Multi-objective Grey Wolf Optimization-multi-kernel-based Extreme Learning Machine Agent Model[J].Bulletin of Engineering Geology and the Environment,2021,80(3):2011-2024.
[12] ZHOU J,LI E M,YANG S,et al.Slope Stability Prediction for Circular Mode Failure Using Gradient Boosting Machine Approach Based on an Updated Database of Case Histories[J].Safety Science,2019,118:505-518.
[13] 兰恒星,周成虎,李焯芬,等.瞬时孔隙水压力作用下的降雨滑坡稳定性响应分析:以香港天然降雨滑坡为例[J].中国科学:E辑,技术科学,2003,33(增):119-136.
LAN Heng-xing,ZHOU Cheng-hu,LI Chao-fen,et al.Rainfall-induced Landslide Stability Analysis in Response to Transient Pore Pressure:A Case Study of Natural Terrain Landslide in Hong Kong[J].Science in China:Series E,Technological Sciences,2003,33(S):119-136.
[14] HE X Z,XU H D,SABETAMAL H,et al.Machine Learning Aided Stochastic Reliability Analysis of Spatially Variable Slopes[J].Computers and Geotechnics,2020,126:103711.
[15] 仉文岗,何昱苇,王鲁琦,等.基于水系分区的滑坡易发性机器学习分析方法:以重庆市奉节县为例[J].地球科学,2022,https://kns.cnki.net/kcms/detail/42.1874.P.20220907.1841.006.html.
ZHANG Wen-gang,HE Yu-wei,WANG Lu-qi,et al.Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division:Case Study of Fengjie County in Chongqing[J].Earth Science,2022,https:∥kns.cnki.net/kcms/detail/42.1874.P.202209-07.1841.006.html.
[16] DIEU T B,TSANGARATOS P,VIET-TIEN N,et al.Comparing the Prediction Performance of a Deep Learning Neural Network Model with Conventional Machine Learning Models in Landslide Susceptibility Assessment[J].Catena,2020,188:104426.
[17] ZHAO H B,YIN S D,RU Z L.Relevance Vector Machine Applied to Slope Stability Analysis[J].International Journal for Numerical and Analytical Methods in Geomechanics,2012,36(5):643-652.
[18] LIN Y,ZHOU K P,LI J L.Prediction of Slope Stabi-lity Using Four Supervised Learning Methods[J].IEEE Access,2018,6:31169-31179.
[19] 孙吉书,夏健超,王建平,等.相关向量机模型在边坡稳定性预测中的应用[J].科学技术与工程,2021,21(28):12234-12242.
SUN Ji-shu,XIA Jian-chao,WANG Jian-ping,et al.Application of Relevance Vector Machine Model in Slope Stability Prediction[J].Science Technology and Engineering,2021,21(28):12234-12242.
[20] 马彦彬,李红蕊,王 林,等.机器学习方法在滑坡易发性评价中的应用[J].土木与环境工程学报,2022,44(1):53-67.
MA Yan-bin,LI Hong-rui,WANG Lin,et al.Machine Learning Algorithms and Techniques for Landslide Susceptibility Investigation:A Literature Review[J].Journal of Civil and Environmental Engineering,2022,44(1):53-67.
[21] FOONG L K,MOAYEDI H.Slope Stability Evaluation Using Neural Network Optimized by Equilibrium Optimization and Vortex Search Algorithm[J].Engineering with Computers,2022,38(S2):1269-1283.
[22] 黄发明,殷坤龙,蒋水华,等.基于聚类分析和支持向量机的滑坡易发性评价[J].岩石力学与工程学报,2018,37(1):156-167.
HUANG Fa-ming,YIN Kun-long,JIANG Shui-hua,et al.Landslide Susceptibility Assessment Based on Clustering Analysis and Support Vector Machine[J].Chinese Journal of Rock Mechanics and Engineering,2018,37(1):156-167.
[23] 温廷新,张 波.露天煤矿边坡稳定性的随机森林预测模型[J].科技导报,2014,32(4/5):105-109.
WEN Ting-xin,ZHANG Bo.Prediction Model for Open-pit Coal Mine Slope Stability Based on Random Forest[J].Science & Technology Review,2014,32(4/5):105-109.
[24] 石菊松.基于遥感和地理信息系统的滑坡风险评估关键技术研究[D].北京:中国地质科学院,2008.
SHI Ju-song.Key Techniques Study of Remote Sensing and Geographic Information System Based Landslide Risk Assessment[D].Beijing:Chinese Academy of Geological Sciences,2008.
[25] 李广信.岩土工程安全系数法稳定分析中的荷载与抗力[J].岩土工程学报,2021,43(5):918-925.
LI Guang-xin.Load and Resistance in Stability Analysis of Geotechnical Engineering with Safety Factor Method[J].Chinese Journal of Geotechnical Engineering,2021,43(5):918-925.
[26] ZHOU J,LI E M,YANG S,et al.Slope Stability Prediction for Circular Mode Failure Using Gradient Boosting Machine Approach Based on an Updated Database of Case Histories[J].Safety Science,2019,118:505-518.
[27] 赵洪波,冯夏庭.支持向量机函数拟合在边坡稳定性估计中的应用[J].岩石力学与工程学报,2003,21(2):241-245.
ZHAO Hong-bo,FENG Xia-ting.Application of Support Vector Machines Function Fitting in Slope Stability Evalution[J].Chinese Journal of Rock Mecha-nics and Engineering,2003,21(2):241-245.
[28] 邹义怀,江成玉,李春辉.人工神经网络在边坡稳定性预测中的应用[J].矿冶,2011,20(4):38-41.
ZOU Yi-huai,JIANG Cheng-yu,LI Chun-hui.Application of Artificial Neural Network in Stability Prediction of Slope[J].Mining and Metallurgy,2011,20(4):38-41.
[29] 匡 野.基于逐类组合支持向量机的边坡稳定性预测研究[J].路基工程,2013(5):73-76.
KUANG Ye.Research on Slope Stability Prediction Based on Class-by-class Combination Support Vector Machine[J].Subgrade Engineering,2013(5):73-76.
[30] 王 佳.基于支持向量机的边坡随机模糊可靠度分析[D].武汉:华中科技大学,2016.
WANG Jia.Slope Fuzzy-random Reliability Analysis Based on Support Vector Machine[D].Wuhan:Huazhong University of Science and Technology,2016.
[31] 张 研,吴哲康,王鹏鹏,等.基于PCA-RVM的露天矿边坡稳定性预测模型[J].矿业研究与开发,2021,41(3):13-18.
ZHANG Yan,WU Zhe-kang,WANG Peng-peng,et al.Prediction Model of Slope Stability of Open Pit Mine Based on PCA-RVM[J].Mining Research and Deve-lopment,2021,41(3):13-18.
[32] 孙伟超,韩 冰.基于PCA-PSO-SVM的边坡稳定性预测[J].河北地质大学学报,2018,41(1):60-64.
SUN Wei-chao,HAN Bing.Slope Stability Prediction Based on PCA-PSO-SVM[J].Journal of Hebei GEO University,2018,41(1):60-64.
[33] 张云雁.基于GS-SVM的边坡稳定性预测模型[J].水利水电技术,2020,51(11):205-209.
ZHANG Yun-yan.GS-SVM-based Prediction Model for Slope Stability[J].Water Resources and Hydropower Engineering,2020,51(11):205-209.
[34] 黄 俊,刘小生.基于GS-PSO-SVM模型的边坡稳定性预测模型[J].中国矿业,2020,29(6):87-91.
HUANG Jun,LIU Xiao-sheng.Slope Stability Prediction Model Based on GS-PSO-SVM Model[J].China Mining Magazine,2020,29(6):87-91.
[35] 王鹏飞.基于GM-RBF组合模型的高路堑边坡稳定性预测研究[J].建筑结构,2021,51(20):140-145.
WANG Peng-fei.Study on Stability Prediction of High Cutting Slope Based on GM-RBF Combination Model[J].Building Structure,2021,51(20):140-145.
[36] 史秀志,周 健,郑 纬,等.边坡稳定性预测的Bayes判别分析方法及应用[J].四川大学学报(工程科学版),2010,42(3):63-68.
SHI Xiu-zhi,ZHOU Jian,ZHENG Wei,et al.Bayes Discriminant Analysis Method and Its Application for Prediction of Slope Stability[J].Journal of Sichuan University(Engineering Science Edition),2010,42(3):63-68.
[37] 付义祥,刘世凯,刘大鹏.RBF神经网络在边坡岩体稳定性中的预测研究[J].武汉理工大学学报(交通科学与工程版),2003,27(2):170-173.
FU Yi-xiang,LIU Shi-kai,LIU Da-peng.Prediction of RBF Neural Network in Slope Rock Stability[J].Journal of Wuhan University of Technology(Tran-sportation Science & Engineering),2003,27(2):170-173.
[38] 乔 朋,梁志强,徐 凯,等.基于机器学习的中小跨径桥梁技术状况评估[J].长安大学学报(自然科学版),2021,41(6):39-52.
QIAO Peng,LIANG Zhi-qiang,XU Kai,et al.Evaluation of Technical Condition of Medium and Small Span Bridge Based on Machine Learning[J].Journal of Chang'an University(Natural Science Edition),2021,41(6):39-52.
[39] 刘 玮,王智伟,袁照旺,等.人工神经网络在商场建筑物冷负荷预测中的应用[J].长安大学学报(建筑与环境科学版),2004,21(4):58-61.
LIU Wei,WANG Zhi-wei,YUAN Zhao-wang,et al.Application of Artificial Neural Network to Predict Building's Cooling Load[J].Journal of Chang'an University(Architecture and Environment Science Edition),2004,21(4):58-61.
[40] 邵 勇,陈从新,鲁祖德,等.基于机器学习的深基坑人字形支护变形预测分析[J].岩土力学,2020,41(增2):1-9.
SHAO Yong,CHEN Cong-xin,LU Zu-de,et al.Prediction and Analysis of Deformation of Deep Foundation Pit Herringbone Retaining Support(HRS)Based on Machine Learning[J].Rock and Soil Mechanics,2020,41(S2):1-9.
[41] 李 龙,刘造保,周宏源,等.基于TBM岩机信息的隧洞断层超前智能感知加权投票模型研究[J].岩石力学与工程学报,2020,39(增2):3403-3411.
LI Long,LIU Zao-bao,ZHOU Hong-yuan,et al.Wei-ghted Voting Model for Advanced Intelligent Perception of Tunnel Faults Based on TBM Rock-machine Information[J].Chinese Journal of Rock Mechanics and Engineering,2020,39(S2):3403-3411.
[42] 黄发明,曹 昱,范宣梅,等.不同滑坡边界及其空间形状对滑坡易发性预测不确定性的影响规律[J].岩石力学与工程学报,2021,40(增2):3227-3240.
HUANG Fa-ming,CAO Yu,FAN Xuan-mei,et al.Effects of Different Landslide Boundaries and Their Spatial Shapes on the Uncertainty of Landslide Susceptibility Prediction[J].Chinese Journal of Rock Mechanics and Engineering,2021,40(S2):3227-3240.
[43] DU J,GLADE T,WOLDAI T,et al.Landslide Susceptibility Assessment Based on an Incomplete Landslide Inventory in the Jilong Valley,Tibet,Chinese Himalayas[J].Engineering Geology,2020,270:105572.
[44] 王 伟,袁雯宇,邹丽芳,等.基于滑坡敏感性评价的库区水动力型滑坡区域综合预警研究[J].岩石力学与工程学报,2022,41(3):479-491.
WANG Wei,YUAN Wen-yu,ZOU Li-fang,et al.Comprehensive Regional-scale Early Warning of Water-induced Landslides in Reservoir Areas Based on Landslide Susceptibility Assessment[J].Chinese Journal of Rock Mechanics and Engineering,2022,41(3):479-491.
[45] 张开放,苏华友,窦 勇.一种基于混淆矩阵的多分类任务准确率评估新方法[J].计算机工程与科学,2021,43(11):1910-1919.
ZHANG Kai-fang,SU Hua-you,DOU Yong.A New Multi-classification Task Accuracy Evaluation Method Based on Confusion Matrix[J].Computer Engineering & Science,2021,43(11):1910-1919.
[46] 武梦婷,陈秋松,齐冲冲.基于机器学习的边坡安全稳定性评价及防护措施[J].工程科学学报,2022,44(2):180-188.
WU Meng-ting,CHEN Qiu-song,QI Chong-chong.Slope Safety,Stability Evaluation,and Protective Measures Based on Machine Learning[J].Chinese Journal of Engineering,2022,44(2):180-188.
[47] 叶为民,孔令伟,胡瑞林,等.膨胀土滑坡与工程边坡新型防治技术与工程示范研究[J].岩土工程学报,2022,44(7):1295-1309.
YE Wei-min,KONG Ling-wei,HU Rui-lin,et al.New Prevention and Treatment Techniques and Their Applications to Landslides and Engineering Slopes of Expansive Soils[J].Chinese Journal of Geotechnical Engineering,2022,44(7):1295-1309.
[48] 徐永福,程 岩,肖 杰,等.膨胀土滑坡和工程边坡新型防治技术研究[J].岩土工程学报,2022,44(7):1281-1294.
XU Yong-fu,CHENG Yan,XIAO Jie,et al.New Prevention and Control Technology for Expansive Soil Slopes[J].Chinese Journal of Geotechnical Engineering,2022,44(7):1281-1294.
[49] 李庶林,赵睿鸣,彭府华,等.基于强度折减法的高陡边坡滑坡治理稳定性分析[J].建筑科学与工程学报,2020,37(1):120-126.
LI Shu-lin,ZHAO Rui-ming,PENG Fu-hua,et al.Stability Analysis of Treatment of High-steep Slope Landslide Based on Strength Reduction Method[J].Journal of Architecture and Civil Engineering,2020,37(1):120-126.

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Last Update: 2022-11-25