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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| Format: | Article |
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Springer Science and Business Media Deutschland GmbH
2021
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| _version_ | 1796856599530176512 |
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| author | Yong, Weixun Zhou, Jian Armaghani, Danial Jahed M. Tahir, M. Tarinejad, Reza Pham, Binh Thai Huynh, Van Van |
| author_facet | Yong, Weixun Zhou, Jian Armaghani, Danial Jahed M. Tahir, M. Tarinejad, Reza Pham, Binh Thai Huynh, Van Van |
| author_sort | Yong, Weixun |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T18:45:27Z |
| format | Article |
| id | utm.eprints-29698 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:45:27Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | utm.eprints-296982022-01-31T08:41:37Z http://eprints.utm.my/29698/ A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles Yong, Weixun Zhou, Jian Armaghani, Danial Jahed M. Tahir, 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 2021-07 Article PeerReviewed Yong, Weixun and Zhou, Jian and Armaghani, Danial Jahed and M. Tahir, 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 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Yong, Weixun Zhou, Jian Armaghani, Danial Jahed M. Tahir, 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 |
| title | 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_short | 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 |
| topic | TA Engineering (General). Civil engineering (General) |
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