Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete
Abstract Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced c...
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Online Access: | http://dx.doi.org/10.1002/suco.202300566 https://onlinelibrary.wiley.com/doi/pdf/10.1002/suco.202300566 |
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crwiley:10.1002/suco.202300566 2024-06-23T07:56:08+00:00 Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete Esmaeili‐Falak, Mahzad Sarkhani Benemaran, Reza 2024 http://dx.doi.org/10.1002/suco.202300566 https://onlinelibrary.wiley.com/doi/pdf/10.1002/suco.202300566 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Structural Concrete volume 25, issue 1, page 716-737 ISSN 1464-4177 1751-7648 journal-article 2024 crwiley https://doi.org/10.1002/suco.202300566 2024-06-11T04:43:46Z Abstract Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression () methods to forecast the frost durability () of on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (), Grey wolf optimization (), and Henry Gas Solubility Optimization () were employed for indicating optimal values of key parameters. The results depicted that all developed models have capability in predicting the of in cold regions. The values of as a comprehensive index depicted that the model has the higher value at 0.0571 as the weakest model, then at 0.0312 recognized as the second‐highest model, and finally the system at 0.0224 mentioned as outperformed model. and approaches were likewise capable of accurately forecasting the of in cold regions, but the created method outperformed them all when taking into account the explanations and justifications. Article in Journal/Newspaper permafrost Wiley Online Library Structural Concrete 25 1 716 737 |
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Wiley Online Library |
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English |
description |
Abstract Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression () methods to forecast the frost durability () of on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (), Grey wolf optimization (), and Henry Gas Solubility Optimization () were employed for indicating optimal values of key parameters. The results depicted that all developed models have capability in predicting the of in cold regions. The values of as a comprehensive index depicted that the model has the higher value at 0.0571 as the weakest model, then at 0.0312 recognized as the second‐highest model, and finally the system at 0.0224 mentioned as outperformed model. and approaches were likewise capable of accurately forecasting the of in cold regions, but the created method outperformed them all when taking into account the explanations and justifications. |
format |
Article in Journal/Newspaper |
author |
Esmaeili‐Falak, Mahzad Sarkhani Benemaran, Reza |
spellingShingle |
Esmaeili‐Falak, Mahzad Sarkhani Benemaran, Reza Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete |
author_facet |
Esmaeili‐Falak, Mahzad Sarkhani Benemaran, Reza |
author_sort |
Esmaeili‐Falak, Mahzad |
title |
Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete |
title_short |
Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete |
title_full |
Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete |
title_fullStr |
Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete |
title_full_unstemmed |
Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete |
title_sort |
application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete |
publisher |
Wiley |
publishDate |
2024 |
url |
http://dx.doi.org/10.1002/suco.202300566 https://onlinelibrary.wiley.com/doi/pdf/10.1002/suco.202300566 |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Structural Concrete volume 25, issue 1, page 716-737 ISSN 1464-4177 1751-7648 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/suco.202300566 |
container_title |
Structural Concrete |
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25 |
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1 |
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716 |
op_container_end_page |
737 |
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1802649023597772800 |