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An optimized model based on the gene expression programming method to estimate safety factor of rock slopes

Author

Listed:
  • Arsalan Mahmoodzadeh

    (University of Halabja)

  • Abed Alanazi

    (Prince Sattam Bin Abdulaziz University)

  • Adil Hussein Mohammed

    (Cihan University-Erbil)

  • Ahmed Babeker Elhag

    (King Khalid University)

  • Abdullah Alqahtani

    (Prince Sattam Bin Abdulaziz University)

  • Shtwai Alsubai

    (Prince Sattam Bin Abdulaziz University)

Abstract

Geotechnical engineers must place a high priority on the analysis and forecasting of slope stability to prevent the disasters that can result from a failed slope. As a result, it is crucial to accurately estimate slope stability in order to ensure the project's success. This sort of information is indispensable in the early stages of concept and design, when important decisions must be made. In this study, an optimized GEP-based model for calculating the safety factor of rock slopes (SFRS) was proposed. For this purpose, a variety of rock slopes for circular failure mode were analyzed using the PLAXIS software to generate 325 datasets. In the datasets, six effective parameters on the SFRS including unit weight, friction angle, slope angle, cohesion, pore pressure ratio, and slope height were considered. 80% of the datasets were used for training and 20% for test. As a result of finding the optimal fit between the predictions, an equation for the refined GEP model was derived. Finally, the equation's potential ability to estimate SFRS was approved by comparing its outputs with the actual ones and comparing its behavior with practice. The mutual information sensitivity analysis revealed that the unit weight parameter is the most influential variable in the proposed equation. This model can reduce the uncertainties about the stability of rock slopes and give machine learning development in the field.

Suggested Citation

  • Arsalan Mahmoodzadeh & Abed Alanazi & Adil Hussein Mohammed & Ahmed Babeker Elhag & Abdullah Alqahtani & Shtwai Alsubai, 2024. "An optimized model based on the gene expression programming method to estimate safety factor of rock slopes," 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. 120(2), pages 1665-1688, January.
  • Handle: RePEc:spr:nathaz:v:120:y:2024:i:2:d:10.1007_s11069-023-06152-1
    DOI: 10.1007/s11069-023-06152-1
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    References listed on IDEAS

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    1. Arsalan Mahmoodzadeh & Mokhtar Mohammadi & Hunar Farid Hama Ali & Hawkar Hashim Ibrahim & Sazan Nariman Abdulhamid & Hamid Reza Nejati, 2022. "Prediction of safety factors for slope stability: comparison of machine learning 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. 111(2), pages 1771-1799, March.
    2. Xianfeng Li & Mayuko Nishio & Kentaro Sugawara & Shoji Iwanaga & Pang-jo Chun, 2023. "Surrogate Model Development for Slope Stability Analysis Using Machine Learning," Sustainability, MDPI, vol. 15(14), pages 1-36, July.
    3. 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.
    4. Zaobao Liu & Jianfu Shao & Weiya Xu & Hongjie Chen & Yu Zhang, 2014. "An extreme learning machine approach for slope stability evaluation and prediction," 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. 73(2), pages 787-804, September.
    5. Shaojun Li & Hong-Bo Zhao & Zhongliang Ru, 2013. "Slope reliability analysis by updated support vector machine and Monte Carlo simulation," 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. 65(1), pages 707-722, January.
    6. 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.
    7. 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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