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

International Journal of Mining Science and Technology 2023年第1期33卷 47-59页

作者： Guangjin Wang Bing Zhao Bisheng Wu Chao Zhang Wenlian Liu Faculty of Land Resources Engineering Kunming University of Science and TechnologyKunming 650093China Yunnan International Technology Transfer Center for Mineral Resources Development and Solid Waste Resource Utilization Kunming 650093China State Key Laboratory of Hydroscience and Engineering Department of Hydraulic EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China China Nonferrous Metals Industry Kunming Survey and Design Research Institute Co.Ltd Kunming 650051China

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.

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

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