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A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consist...

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Main Authors: Yong, Weixun, Zhou, Jian, Armaghani, Danial Jahed, Tahir, M. M., Tarinejad, Reza, Pham, Binh Thai, Huynh, Van Van
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/97668/
http://dx.doi.org/10.1007/s00366-019-00932-9
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spelling my.utm.976682022-10-25T10:30:27Z http://eprints.utm.my/id/eprint/97668/ A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles Yong, Weixun Zhou, Jian Armaghani, Danial Jahed Tahir, M. M. Tarinejad, Reza Pham, Binh Thai Huynh, Van Van TA Engineering (General). Civil engineering (General) The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set. Springer Science and Business Media Deutschland GmbH 2021 Article PeerReviewed Yong, Weixun and Zhou, Jian and Armaghani, Danial Jahed and Tahir, M. M. and Tarinejad, Reza and Pham, Binh Thai and Huynh, Van Van (2021) A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles. Engineering with Computers, 37 (3). pp. 2111-2127. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-019-00932-9 DOI : 10.1007/s00366-019-00932-9
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)
Yong, Weixun
Zhou, Jian
Armaghani, Danial Jahed
Tahir, M. M.
Tarinejad, Reza
Pham, Binh Thai
Huynh, Van Van
A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
description The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set.
format Article
author Yong, Weixun
Zhou, Jian
Armaghani, Danial Jahed
Tahir, M. M.
Tarinejad, Reza
Pham, Binh Thai
Huynh, Van Van
author_facet Yong, Weixun
Zhou, Jian
Armaghani, Danial Jahed
Tahir, M. M.
Tarinejad, Reza
Pham, Binh Thai
Huynh, Van Van
author_sort Yong, Weixun
title A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
title_short A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
title_full A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
title_fullStr A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
title_full_unstemmed A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
title_sort new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2021
url http://eprints.utm.my/id/eprint/97668/
http://dx.doi.org/10.1007/s00366-019-00932-9
_version_ 1748180491504189440
score 13.211869

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