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Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils

By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP)...

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Main Authors: Moayedi, Hossein, Mohammed Abdullahi, Mu’azu, 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/95069/
http://dx.doi.org/10.1007/s00366-019-00834-w
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spelling my.utm.950692022-04-29T22:23:43Z http://eprints.utm.my/id/eprint/95069/ Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils Moayedi, Hossein Mohammed Abdullahi, Mu’azu Nguyen, Hoang A. Rashid, Ahmad Safuan TA Engineering (General). Civil engineering (General) By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP) predictive tool for adjusting the connecting weights and biases in predicting the failure probability using seven settlement key factors, namely unit weight, friction angle, elastic modulus, dilation angle, Poisson’s ratio, applied stress, and setback distance. As the first result, incorporating both HHO and DA metaheuristic algorithms resulted in higher efficiency of the MLP. Moreover, referring to the calculated area under the receiving operating characteristic curve (AUC), as well as the calculated mean square error, the DA-MLP (AUC = 0.942 and MSE = 0.1171) outperforms the HHO-MLP (AUC = 0.915 and MSE = 0.1350) and typical MLP (AUC = 0.890 and MSE = 0.1416). Furthermore, the DA surpassed the HHO in terms of time-effectiveness. Springer Science and Business Media Deutschland GmbH 2021 Article PeerReviewed Moayedi, Hossein and Mohammed Abdullahi, Mu’azu and Nguyen, Hoang and A. Rashid, Ahmad Safuan (2021) Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils. Engineering with Computers, 37 (1). pp. 437-447. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-019-00834-w
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
Mohammed Abdullahi, Mu’azu
Nguyen, Hoang
A. Rashid, Ahmad Safuan
Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils
description By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP) predictive tool for adjusting the connecting weights and biases in predicting the failure probability using seven settlement key factors, namely unit weight, friction angle, elastic modulus, dilation angle, Poisson’s ratio, applied stress, and setback distance. As the first result, incorporating both HHO and DA metaheuristic algorithms resulted in higher efficiency of the MLP. Moreover, referring to the calculated area under the receiving operating characteristic curve (AUC), as well as the calculated mean square error, the DA-MLP (AUC = 0.942 and MSE = 0.1171) outperforms the HHO-MLP (AUC = 0.915 and MSE = 0.1350) and typical MLP (AUC = 0.890 and MSE = 0.1416). Furthermore, the DA surpassed the HHO in terms of time-effectiveness.
format Article
author Moayedi, Hossein
Mohammed Abdullahi, Mu’azu
Nguyen, Hoang
A. Rashid, Ahmad Safuan
author_facet Moayedi, Hossein
Mohammed Abdullahi, Mu’azu
Nguyen, Hoang
A. Rashid, Ahmad Safuan
author_sort Moayedi, Hossein
title Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils
title_short Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils
title_full Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils
title_fullStr Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils
title_full_unstemmed Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils
title_sort comparison of dragonfly algorithm and harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2021
url http://eprints.utm.my/id/eprint/95069/
http://dx.doi.org/10.1007/s00366-019-00834-w
_version_ 1732945428388773888
score 13.211869

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