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Application of Harmony Search Algorithm to Slope Stability Analysis

Author

Listed:
  • Sina Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Sami Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Zong Woo Geem

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

  • Tae-Hyung Kim

    (Department of Civil Engineering, Korea Maritime and Ocean University, Pusan 49112, Korea)

  • Reza Mikaeil

    (Department of Mining and Engineering, Faculty of Environment, Urmia University of Technology, Urmia, Iran)

  • Luigi Pugliese

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Antonello Troncone

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

Abstract

Slope stability analysis is undoubtedly one of the most complex problems in geotechnical engineering and its study plays a paramount role in mitigating the risk associated with the occurrence of a landslide. This problem is commonly tackled by using limit equilibrium methods or advanced numerical techniques to assess the slope safety factor or, sometimes, even the displacement field of the slope. In this study, as an alternative approach, an attempt to assess the stability condition of homogeneous slopes was made using a machine learning (ML) technique. Specifically, a meta-heuristic algorithm (Harmony Search (HS) algorithm) and K-means algorithm were employed to perform a clustering analysis by considering two different classes, depending on whether a slope was unstable or stable. To achieve the purpose of this study, a database made up of 19 case studies with 6 model inputs including unit weight, intercept cohesion, angle of shearing resistance, slope angle, slope height and pore pressure ratio and one output (i.e., the slope safety factor) was established. Referring to this database, 17 out of 19 slopes were categorized correctly. Moreover, the obtained results showed that, referring to the considered database, the intercept cohesion was the most significant parameter in defining the class of each slope, whereas the unit weight had the smallest influence. Finally, the obtained results showed that the Harmony Search algorithm is an efficient approach for training K-means algorithms.

Suggested Citation

  • Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Zong Woo Geem & Tae-Hyung Kim & Reza Mikaeil & Luigi Pugliese & Antonello Troncone, 2021. "Application of Harmony Search Algorithm to Slope Stability Analysis," Land, MDPI, vol. 10(11), pages 1-12, November.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1250-:d:679150
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    References listed on IDEAS

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    1. Reza Mikaeil & Sina Shaffiee Haghshenas & Zoheir Sedaghati, 2019. "Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel)," 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. 97(3), pages 1099-1113, July.
    2. Francesco Paola & Maurizio Giugni & Davide Portolano, 2017. "Pressure Management Through Optimal Location and Setting of Valves in Water Distribution Networks Using a Music-Inspired Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1517-1533, March.
    3. Kapil Gnawali & Kuk Heon Han & Zong Woo Geem & Kyung Soo Jun & Kyung Taek Yum, 2019. "Economic Dispatch Optimization of Multi-Water Resources: A Case Study of an Island in South Korea," Sustainability, MDPI, vol. 11(21), pages 1-18, October.
    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. 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.
    6. Giuseppe Guido & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Alessandro Vitale & Vittorio Astarita & Ashkan Shafiee Haghshenas, 2020. "Feasibility of Stochastic Models for Evaluation of Potential Factors for Safety: A Case Study in Southern Italy," Sustainability, MDPI, vol. 12(18), pages 1-24, September.
    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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