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Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning

Author

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  • Cuiying Zhou

    (School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Engineering Research Centre for Major Infrastructure Safety, Guangzhou 510275, China)

  • Jinwu Ouyang

    (School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Engineering Research Centre for Major Infrastructure Safety, Guangzhou 510275, China)

  • Zhen Liu

    (School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Engineering Research Centre for Major Infrastructure Safety, Guangzhou 510275, China)

  • Lihai Zhang

    (Department of Infrastructure Engineering, The University of Melbourne, Melbourne 3010, Australia)

Abstract

Maintaining the stability of highway soft rock slopes is of critical importance for ensuring the safety of road networks. Although much research has been carried out to assess the stability of individual soft rock slope, the goal of efficient and effective risk management focusing on multiple highway soft rock slopes has not been fully achieved due to the many complex factors involved and the interactions among these factors. In the present study, a machine learning algorithm based on a fuzzy neural network (FNN) and a comprehensive evaluation method based on the FNN is developed, in order to identify and issue early warnings regarding the risks induced by soft rock slopes along highways, in an efficient and effective way. Using a large amount of collected soft rock slope information as training and validation data, an FNN-based risk identification model is first developed to identify the risk level of individual soft rock slope based on the meteorological conditions, topographical and geomorphological factors, geotechnical properties, and the measured horizontal displacement. An FNN-based comprehensive evaluation method is then developed, in order to quantify the risk level of a soft rock slope group according to the slope, road and external factors. The results show that the risk level identification accuracy obtained based on validation of the FNN model was higher than 90%, and the model showed a good training effect. On this basis, we further made early warnings of the risks of soft rock slope groups. The proposed early-warning model can quickly and accurately evaluate the risk posed by multiple soft rock slopes to a highway, thereby ensuring the safety of the highway.

Suggested Citation

  • Cuiying Zhou & Jinwu Ouyang & Zhen Liu & Lihai Zhang, 2022. "Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3367-:d:770293
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    References listed on IDEAS

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    Cited by:

    1. Gongfa Chen & Wei Deng & Mansheng Lin & Jianbin Lv, 2023. "Slope stability analysis based on convolutional neural network and digital twin," 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. 118(2), pages 1427-1443, September.
    2. Yi Gao & Zhen Liu & Cuiying Zhou, 2023. "Classification and Zoning of Improved Materials of Weathered Redbed Soil in China Based on the Integrity of Mud Skin," Sustainability, MDPI, vol. 15(8), pages 1-18, April.

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