Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases

作     者：Guangjin Wang Bing Zhao Bisheng Wu Chao Zhang Wenlian Liu Guangjin Wang;Bing Zhao;Bisheng Wu;Chao Zhang;Wenlian Liu

作者机构：Faculty of Land Resources EngineeringKunming University of Science and TechnologyKunming 650093China Yunnan International Technology Transfer Center for Mineral Resources Development and Solid Waste Resource UtilizationKunming 650093China State Key Laboratory of Hydroscience and EngineeringDepartment of Hydraulic EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Geomechanics and Geotechnical EngineeringInstitute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China China Nonferrous Metals Industry Kunming Survey and Design Research Institute Co.LtdKunming 650051China 

出 版 物：《International Journal of Mining Science and Technology》 (矿业科学技术学报（英文版）)

年 卷 期：2023年第33卷第1期

页      面：47-59页

核心收录：

学科分类：0711[理学-系统科学] 081901[工学-采矿工程] 0819[工学-矿业工程] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：by the National Natural Science Foundation of China(No.52174114) the State Key Laboratory of Hydroscience and Engineering of Tsinghua University(No.61010101218). 

主　　题：Slope stability prediction Machine learning algorithm Dimensionality reduction visualization Random cross validation Coefficient of variation 

摘      要：Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.