Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms
Friction capacity is a principal characteristic in designing driven piles. Considering the complexities in analyzing the behavior of piles, many studies have recommended the use of machine learning for this purpose. However, the used methodologies need to be updated and improved with respect to rece...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509523006447 |
| _version_ | 1797454564036706304 |
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| author | Liu Jie Parisa Sahraeian Kseniya I. Zykova Majid Mirahmadi Moncef L. Nehdi |
| author_facet | Liu Jie Parisa Sahraeian Kseniya I. Zykova Majid Mirahmadi Moncef L. Nehdi |
| author_sort | Liu Jie |
| collection | DOAJ |
| description | Friction capacity is a principal characteristic in designing driven piles. Considering the complexities in analyzing the behavior of piles, many studies have recommended the use of machine learning for this purpose. However, the used methodologies need to be updated and improved with respect to recent computational advances such as the development of optimization algorithms. In this work, three metaheuristic algorithms, namely equilibrium optimizer (EO), biogeography-based optimization (BBO), and salp swarm algorithm (SSA) are deployed to optimize an artificial neural network (ANN) for predicting pile friction capacity based on pile geometry, effective stress, and shear strength. The findings indicate the suitability of the proposed algorithms. More specifically, in the training phase, the ANN supervised by SSA yielded the most accurate results, whereas in the testing phase, the BBO-ANN outperformed the two other models. The calculated mean absolute error, Pearson correlation coefficient, and root mean square error for the models are as follows: 6.0740, 0.9385, and 7.0678 for the EO-ANN, 6.1450, 0.9440, and 6.7343 for the BBO-ANN, and 5.9684, 0.9395, and 7.1322 for the SSA-ANN. It is shown that both SSA-ANN and BBO-ANN can serve as efficient tools for the reliable design of driven piles, providing efficient computational intelligence alternatives to traditional design methods. |
| first_indexed | 2024-03-09T15:39:51Z |
| format | Article |
| id | doaj.art-642d88d832d947b39ef4e6e63cd24702 |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-09T15:39:51Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-642d88d832d947b39ef4e6e63cd247022023-11-25T04:48:55ZengElsevierCase Studies in Construction Materials2214-50952023-12-0119e02464Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithmsLiu Jie0Parisa Sahraeian1Kseniya I. Zykova2Majid Mirahmadi3Moncef L. Nehdi4School of Mechanical Engineering, Nanjing Vocational University of Industry Technology, Nanjing, 210023, ChinaSchool of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, USADepartment of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus, Kuwait; Department of Safety in cyberworld, Bauman Moscow State Technical University Moscow, RussiaDepartment of Civil Engineering, Payame Noor University, Tehran, IranDepartment of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada; Corresponding author.Friction capacity is a principal characteristic in designing driven piles. Considering the complexities in analyzing the behavior of piles, many studies have recommended the use of machine learning for this purpose. However, the used methodologies need to be updated and improved with respect to recent computational advances such as the development of optimization algorithms. In this work, three metaheuristic algorithms, namely equilibrium optimizer (EO), biogeography-based optimization (BBO), and salp swarm algorithm (SSA) are deployed to optimize an artificial neural network (ANN) for predicting pile friction capacity based on pile geometry, effective stress, and shear strength. The findings indicate the suitability of the proposed algorithms. More specifically, in the training phase, the ANN supervised by SSA yielded the most accurate results, whereas in the testing phase, the BBO-ANN outperformed the two other models. The calculated mean absolute error, Pearson correlation coefficient, and root mean square error for the models are as follows: 6.0740, 0.9385, and 7.0678 for the EO-ANN, 6.1450, 0.9440, and 6.7343 for the BBO-ANN, and 5.9684, 0.9395, and 7.1322 for the SSA-ANN. It is shown that both SSA-ANN and BBO-ANN can serve as efficient tools for the reliable design of driven piles, providing efficient computational intelligence alternatives to traditional design methods.http://www.sciencedirect.com/science/article/pii/S2214509523006447Driven pilesFriction capacityNeural simulationMetaheuristic schemes |
| spellingShingle | Liu Jie Parisa Sahraeian Kseniya I. Zykova Majid Mirahmadi Moncef L. Nehdi Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms Case Studies in Construction Materials Driven piles Friction capacity Neural simulation Metaheuristic schemes |
| title | Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms |
| title_full | Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms |
| title_fullStr | Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms |
| title_full_unstemmed | Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms |
| title_short | Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms |
| title_sort | predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms |
| topic | Driven piles Friction capacity Neural simulation Metaheuristic schemes |
| url | http://www.sciencedirect.com/science/article/pii/S2214509523006447 |
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