Application of Harmony Search Algorithm to Slope Stability Analysis

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 te...

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Autores principales:	Sina Shaffiee Haghshenas, Sami Shaffiee Haghshenas, Zong Woo Geem, Tae-Hyung Kim, Reza Mikaeil, Luigi Pugliese, Antonello Troncone
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning K-means algorithm harmony search clustering analysis slope stability Agriculture S
Acceso en línea:	https://doaj.org/article/938f3205e6464ec88f535fdabdadb5f4
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id	oai:doaj.org-article:938f3205e6464ec88f535fdabdadb5f4
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spelling	oai:doaj.org-article:938f3205e6464ec88f535fdabdadb5f42021-11-25T18:09:59ZApplication of Harmony Search Algorithm to Slope Stability Analysis10.3390/land101112502073-445Xhttps://doaj.org/article/938f3205e6464ec88f535fdabdadb5f42021-11-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1250https://doaj.org/toc/2073-445XSlope 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.Sina Shaffiee HaghshenasSami Shaffiee HaghshenasZong Woo GeemTae-Hyung KimReza MikaeilLuigi PuglieseAntonello TronconeMDPI AGarticlemachine learningK-means algorithmharmony searchclustering analysisslope stabilityAgricultureSENLand, Vol 10, Iss 1250, p 1250 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning K-means algorithm harmony search clustering analysis slope stability Agriculture S
spellingShingle	machine learning K-means algorithm harmony search clustering analysis slope stability Agriculture S Sina Shaffiee Haghshenas Sami Shaffiee Haghshenas Zong Woo Geem Tae-Hyung Kim Reza Mikaeil Luigi Pugliese Antonello Troncone Application of Harmony Search Algorithm to Slope Stability Analysis
description	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.
format	article
author	Sina Shaffiee Haghshenas Sami Shaffiee Haghshenas Zong Woo Geem Tae-Hyung Kim Reza Mikaeil Luigi Pugliese Antonello Troncone
author_facet	Sina Shaffiee Haghshenas Sami Shaffiee Haghshenas Zong Woo Geem Tae-Hyung Kim Reza Mikaeil Luigi Pugliese Antonello Troncone
author_sort	Sina Shaffiee Haghshenas
title	Application of Harmony Search Algorithm to Slope Stability Analysis
title_short	Application of Harmony Search Algorithm to Slope Stability Analysis
title_full	Application of Harmony Search Algorithm to Slope Stability Analysis
title_fullStr	Application of Harmony Search Algorithm to Slope Stability Analysis
title_full_unstemmed	Application of Harmony Search Algorithm to Slope Stability Analysis
title_sort	application of harmony search algorithm to slope stability analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/938f3205e6464ec88f535fdabdadb5f4
work_keys_str_mv	AT sinashaffieehaghshenas applicationofharmonysearchalgorithmtoslopestabilityanalysis AT samishaffieehaghshenas applicationofharmonysearchalgorithmtoslopestabilityanalysis AT zongwoogeem applicationofharmonysearchalgorithmtoslopestabilityanalysis AT taehyungkim applicationofharmonysearchalgorithmtoslopestabilityanalysis AT rezamikaeil applicationofharmonysearchalgorithmtoslopestabilityanalysis AT luigipugliese applicationofharmonysearchalgorithmtoslopestabilityanalysis AT antonellotroncone applicationofharmonysearchalgorithmtoslopestabilityanalysis
_version_	1718411572654637056

Application of Harmony Search Algorithm to Slope Stability Analysis

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