Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete
Ever since their presentation in the late 80s, self-compacting concrete (SCC) has been well received by researchers. SCC can flow under their weight and exhibit high workability. Nonetheless, their nonlinear behavior has made the prediction of their mix properties more demanding. Furthermore, the co...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/9887803 |
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| author | Amir Andalib Babak Aminnejad Alireza Lork |
| author_facet | Amir Andalib Babak Aminnejad Alireza Lork |
| author_sort | Amir Andalib |
| collection | DOAJ |
| description | Ever since their presentation in the late 80s, self-compacting concrete (SCC) has been well received by researchers. SCC can flow under their weight and exhibit high workability. Nonetheless, their nonlinear behavior has made the prediction of their mix properties more demanding. Furthermore, the complex relationship between mixed proportions and rheological and mechanical properties of SCC renders their behavior prediction challenging. Soft computing approaches have been shown to optimize and reduce uncertainties, and therefore in this paper, we aim to address these challenges by employing artificial neural network (ANN) models optimized using the grey wolf optimizer (GWO) algorithm. The optimized model proved to be more accurate than genetic algorithms and multiple linear regression models. The results indicate that the four most influential parameters on the compressive strength of SCC are the cement content, ground granulated blast furnace slag, rice husk ash, and fly ash. |
| format | Article |
| id | doaj-art-798bc62bef3c4f6488faabbccc0ad98e |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-798bc62bef3c4f6488faabbccc0ad98e2025-08-20T03:35:48ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/9887803Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting ConcreteAmir Andalib0Babak Aminnejad1Alireza Lork2Department of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringEver since their presentation in the late 80s, self-compacting concrete (SCC) has been well received by researchers. SCC can flow under their weight and exhibit high workability. Nonetheless, their nonlinear behavior has made the prediction of their mix properties more demanding. Furthermore, the complex relationship between mixed proportions and rheological and mechanical properties of SCC renders their behavior prediction challenging. Soft computing approaches have been shown to optimize and reduce uncertainties, and therefore in this paper, we aim to address these challenges by employing artificial neural network (ANN) models optimized using the grey wolf optimizer (GWO) algorithm. The optimized model proved to be more accurate than genetic algorithms and multiple linear regression models. The results indicate that the four most influential parameters on the compressive strength of SCC are the cement content, ground granulated blast furnace slag, rice husk ash, and fly ash.http://dx.doi.org/10.1155/2022/9887803 |
| spellingShingle | Amir Andalib Babak Aminnejad Alireza Lork Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete Applied Computational Intelligence and Soft Computing |
| title | Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete |
| title_full | Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete |
| title_fullStr | Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete |
| title_full_unstemmed | Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete |
| title_short | Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete |
| title_sort | grey wolf optimizer based anns to predict the compressive strength of self compacting concrete |
| url | http://dx.doi.org/10.1155/2022/9887803 |
| work_keys_str_mv | AT amirandalib greywolfoptimizerbasedannstopredictthecompressivestrengthofselfcompactingconcrete AT babakaminnejad greywolfoptimizerbasedannstopredictthecompressivestrengthofselfcompactingconcrete AT alirezalork greywolfoptimizerbasedannstopredictthecompressivestrengthofselfcompactingconcrete |