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

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

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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 mod-els 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 factoriza-tion(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning meth-ods,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 unnec-essary 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.

Slope stability predictionMachine learning algorithmDimensionality reduction visualizationRandom cross validationCoefficient of variation

Guangjin Wang、Bing Zhao、Bisheng Wu、Chao Zhang、Wenlian Liu

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Faculty of Land Resources Engineering,Kunming University of Science and Technology,Kunming 650093,China

Yunnan International Technology Transfer Center for Mineral Resources Development and Solid Waste Resource Utilization,Kunming 650093,China

State Key Laboratory of Hydroscience and Engineering,Department of Hydraulic Engineering,Tsinghua University,Beijing 100084,China

State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan 430071,China

China Nonferrous Metals Industry Kunming Survey and Design Research Institute Co.Ltd,Kunming 650051,China

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National Natural Science Foundation of ChinaState Key Laboratory of Hydroscience and Engineering of Tsinghua University

5217411461010101218

2023

矿业科学技术学报(英文版)
中国矿业大学

矿业科学技术学报(英文版)

CSTPCDCSCD北大核心EI
影响因子：1.222
ISSN：2095-2686
年,卷(期)：2023.33(1)
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