Application of Harmony Search Algorithm to Slope Stability Analysis

Sina Shaffiee Haghshenas, Sami Shaffiee Haghshenas, Zong Woo Geem, Tae-Hyung Kim, Reza Mikaeil, Luigi Pugliese and Antonello Troncone
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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

Land, 2021, vol. 10, issue 11, 1-12

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.

Keywords: machine learning; K-means algorithm; harmony search; clustering analysis; slope stability (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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