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Slope stability classification under seismic conditions using several tree-based intelligent techniques

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts...

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Main Authors:	Asteris, Panagiotis G., Rizal, Fariz Iskandar Mohd, Koopialipoor, Mohammadreza, Roussis, Panayiotis C., Ferentinou, Maria, Armaghani, Danial Jahed, Gordan, Behrouz
Format:	Article
Published:	MDPI 2022
Subjects:	QC Physics QD Chemistry TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/33387/
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spelling	my.um.eprints.333872022-08-09T07:04:18Z http://eprints.um.edu.my/33387/ Slope stability classification under seismic conditions using several tree-based intelligent techniques Asteris, Panagiotis G. Rizal, Fariz Iskandar Mohd Koopialipoor, Mohammadreza Roussis, Panayiotis C. Ferentinou, Maria Armaghani, Danial Jahed Gordan, Behrouz QC Physics QD Chemistry TA Engineering (General). Civil engineering (General) Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions. MDPI 2022-02 Article PeerReviewed Asteris, Panagiotis G. and Rizal, Fariz Iskandar Mohd and Koopialipoor, Mohammadreza and Roussis, Panayiotis C. and Ferentinou, Maria and Armaghani, Danial Jahed and Gordan, Behrouz (2022) Slope stability classification under seismic conditions using several tree-based intelligent techniques. Applied Sciences-Basel, 12 (3). ISSN 2076-3417, DOI https://doi.org/10.3390/app12031753 <https://doi.org/10.3390/app12031753>. 10.3390/app12031753
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QC Physics QD Chemistry TA Engineering (General). Civil engineering (General)
spellingShingle	QC Physics QD Chemistry TA Engineering (General). Civil engineering (General) Asteris, Panagiotis G. Rizal, Fariz Iskandar Mohd Koopialipoor, Mohammadreza Roussis, Panayiotis C. Ferentinou, Maria Armaghani, Danial Jahed Gordan, Behrouz Slope stability classification under seismic conditions using several tree-based intelligent techniques
description	Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions.
format	Article
author	Asteris, Panagiotis G. Rizal, Fariz Iskandar Mohd Koopialipoor, Mohammadreza Roussis, Panayiotis C. Ferentinou, Maria Armaghani, Danial Jahed Gordan, Behrouz
author_facet	Asteris, Panagiotis G. Rizal, Fariz Iskandar Mohd Koopialipoor, Mohammadreza Roussis, Panayiotis C. Ferentinou, Maria Armaghani, Danial Jahed Gordan, Behrouz
author_sort	Asteris, Panagiotis G.
title	Slope stability classification under seismic conditions using several tree-based intelligent techniques
title_short	Slope stability classification under seismic conditions using several tree-based intelligent techniques
title_full	Slope stability classification under seismic conditions using several tree-based intelligent techniques
title_fullStr	Slope stability classification under seismic conditions using several tree-based intelligent techniques
title_full_unstemmed	Slope stability classification under seismic conditions using several tree-based intelligent techniques
title_sort	slope stability classification under seismic conditions using several tree-based intelligent techniques
publisher	MDPI
publishDate	2022
url	http://eprints.um.edu.my/33387/
_version_	1740826027055120384
score	13.211869

Slope stability classification under seismic conditions using several tree-based intelligent techniques

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