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A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability

Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing simple predictive models does not satisfy the required accuracy in analysing the stability of the slopes. Hence, the main objective of t...

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Main Authors: Moayedi, Hossein, Osouli, Abdolreza, Nguyen, Hoang, A. Rashid, Ahmad Safuan
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2021
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/97737/
http://dx.doi.org/10.1007/s00366-019-00828-8
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spelling my.utm.977372022-10-31T07:06:27Z http://eprints.utm.my/id/eprint/97737/ A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability Moayedi, Hossein Osouli, Abdolreza Nguyen, Hoang A. Rashid, Ahmad Safuan TA Engineering (General). Civil engineering (General) Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing simple predictive models does not satisfy the required accuracy in analysing the stability of the slopes. Hence, the main objective of this study is to introduce a novel metaheuristic optimization namely Harris hawks’ optimization (HHO) for enhancing the accuracy of the conventional multilayer perceptron technique in predicting the factor of safety in the presence of rigid foundations. In this way, four slope stability conditioning factors, namely slope angle, the position of the rigid foundation, the strength of the soil, and applied surcharge are considered. Remarkably, the main contribution of this algorithm to the problem of slope stability lies in adjusting the computational weights of these conditioning factors. The results showed that using the HHO increases the prediction accuracy of the ANN for analysing slopes with unseen conditions. In this regard, it led to reducing the root mean square error and mean absolute error criteria by 20.47% and 26.97%, respectively. Moreover, the correlation between the actual values of the safety factor and the outputs of the HHO–ANN (R2 = 0.9253) was more significant than the ANN (R2 = 0.8220). Finally, an HHO-based predictive formula is also presented to be used for similar applications. Springer Science and Business Media Deutschland GmbH 2021 Article PeerReviewed Moayedi, Hossein and Osouli, Abdolreza and Nguyen, Hoang and A. Rashid, Ahmad Safuan (2021) A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability. Engineering with Computers, 37 (1). pp. 369-379. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-019-00828-8 DOI : 10.1007/s00366-019-00828-8
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Moayedi, Hossein
Osouli, Abdolreza
Nguyen, Hoang
A. Rashid, Ahmad Safuan
A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability
description Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing simple predictive models does not satisfy the required accuracy in analysing the stability of the slopes. Hence, the main objective of this study is to introduce a novel metaheuristic optimization namely Harris hawks’ optimization (HHO) for enhancing the accuracy of the conventional multilayer perceptron technique in predicting the factor of safety in the presence of rigid foundations. In this way, four slope stability conditioning factors, namely slope angle, the position of the rigid foundation, the strength of the soil, and applied surcharge are considered. Remarkably, the main contribution of this algorithm to the problem of slope stability lies in adjusting the computational weights of these conditioning factors. The results showed that using the HHO increases the prediction accuracy of the ANN for analysing slopes with unseen conditions. In this regard, it led to reducing the root mean square error and mean absolute error criteria by 20.47% and 26.97%, respectively. Moreover, the correlation between the actual values of the safety factor and the outputs of the HHO–ANN (R2 = 0.9253) was more significant than the ANN (R2 = 0.8220). Finally, an HHO-based predictive formula is also presented to be used for similar applications.
format Article
author Moayedi, Hossein
Osouli, Abdolreza
Nguyen, Hoang
A. Rashid, Ahmad Safuan
author_facet Moayedi, Hossein
Osouli, Abdolreza
Nguyen, Hoang
A. Rashid, Ahmad Safuan
author_sort Moayedi, Hossein
title A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability
title_short A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability
title_full A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability
title_fullStr A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability
title_full_unstemmed A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability
title_sort novel harris hawks' optimization and k-fold cross-validation predicting slope stability
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2021
url http://eprints.utm.my/id/eprint/97737/
http://dx.doi.org/10.1007/s00366-019-00828-8
_version_ 1748703137709948928
score 13.211869

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