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Slope Stability Prediction Method Based on Intelligent Optimization and Machine Learning Algorithms

Author

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  • Yukun Yang

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
    School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    High-Tech Research Center for Open-Pit Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Wei Zhou

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
    School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    High-Tech Research Center for Open-Pit Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Izhar Mithal Jiskani

    (Department of Mining Engineering, Balochistan Campus, National University of Sciences and Technology, Quetta 87300, Pakistan)

  • Xiang Lu

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
    School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    High-Tech Research Center for Open-Pit Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Zhiming Wang

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
    School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    High-Tech Research Center for Open-Pit Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Boyu Luan

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
    School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    High-Tech Research Center for Open-Pit Mines, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Slope engineering is a type of complex system engineering that is mostly involved in water conservancy and civil and mining engineering. Moreover, the link between slope stability and engineering safety is quite close. This study took the stable state of the slope as the prediction object and used the unit weight, cohesion, internal friction angle, pore water pressure coefficient, slope angle, and slope height as prediction indices to analyze the slope stability based on the collection of 117 slope data points. The genetic algorithm was used to solve the hyperparameters of machine learning algorithms by simulating the phenomena of reproduction, hybridization, and mutation in the natural selection and natural genetic processes. Five algorithms were used, including the support vector machine, random forest, nearest neighbor, decision tree, and gradient boosting machine models. Finally, all of the obtained stability prediction results were compared. The prediction outcomes were analyzed using the confusion matrix, receiver characteristic operator (ROC), and area under the curve (AUC) value. The AUC values of all machine learning prediction results were between 0.824 and 0.964, showing excellent performance. Considering the AUC value, accuracy, and other factors, the random forest algorithm with KS cutoff was determined to be the optimal model, and the relative importance of the influencing variables was studied. The results show that cohesion was the factor that most affects slope stability, and the influence factor was 0.327. This study proves the effectiveness of the integrated techniques for slope stability prediction, makes essential suggestions for future slope stability analysis, and may be extensively applied in other industrial projects.

Suggested Citation

  • Yukun Yang & Wei Zhou & Izhar Mithal Jiskani & Xiang Lu & Zhiming Wang & Boyu Luan, 2023. "Slope Stability Prediction Method Based on Intelligent Optimization and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1169-:d:1028784
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    References listed on IDEAS

    as
    1. Min-Yuan Cheng & Nhat-Duc Hoang, 2015. "Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 1961-1978, September.
    2. P. Lu & M. Rosenbaum, 2003. "Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 383-398, November.
    3. Shakti Suman & S. Z. Khan & S. K. Das & S. K. Chand, 2016. "Slope stability analysis using artificial intelligence techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 727-748, November.
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    1. Muhammad Ali & Naseer Muhammad Khan & Qiangqiang Gao & Kewang Cao & Danial Jahed Armaghani & Saad S. Alarifi & Hafeezur Rehman & Izhar Mithal Jiskani, 2023. "Prediction of Coal Dilatancy Point Using Acoustic Emission Characteristics: Insight Experimental and Artificial Intelligence Approaches," Mathematics, MDPI, vol. 11(6), pages 1-25, March.

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