Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA
Self-Compacting Concrete (SCC) has been widely utilized in construction projects and academic research due to its environmentally friendly components, such as fly ash and superplasticizers, which reduce water requirements. SCC’s ability to self-deposit eliminates the need for vibration, resulting in...
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Bilijipub publisher
2024-09-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_206702_a23758fe6e0474dc714cddf3a20e697a.pdf |
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| author | Francisca Blanco Ye Woo |
| author_facet | Francisca Blanco Ye Woo |
| author_sort | Francisca Blanco |
| collection | DOAJ |
| description | Self-Compacting Concrete (SCC) has been widely utilized in construction projects and academic research due to its environmentally friendly components, such as fly ash and superplasticizers, which reduce water requirements. SCC’s ability to self-deposit eliminates the need for vibration, resulting in cost and energy savings. However, some experts are hesitant about its broader application due to insufficient training in modern materials. Accurately assessing construction aggregates' compressive strength (CS) ensures structural safety. Soft computing methods, which offer a cost-effective and highly accurate alternative to experimental techniques, have attracted interest in modeling dependent variables. This paper presents a novel approach by combining a Support Vector Machine (SVM) with advanced optimization algorithms to estimate the CS of SCC mixtures accurately. The significance of this approach lies in the ability of the optimization algorithms to enhance the performance of the SVM, yielding more precise predictions and addressing the limitations of traditional methods. The developed models were evaluated using several performance metrics, with results showing a strong correlation between predicted and actual values, achieving an R² of 97.3%. Furthermore, the root mean square error (RMSE) was calculated at 3.81 MPa, demonstrating the effectiveness of the proposed method in predicting SCC’s compressive strength with high accuracy. |
| format | Article |
| id | doaj-art-dac74a7aa56241f79d8c47fe67f1b1de |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-dac74a7aa56241f79d8c47fe67f1b1de2025-02-12T08:48:04ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-09-010030311410.22034/aeis.2024.470005.1206206702Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOAFrancisca Blanco0Ye Woo1Faculty of Engineering, University of Mondragon, Arrasate, 20500, SpainDepartment of Artificial Intelligence, University of Ajou, Suwon-si, Gyeonggi-do, 16499, Republic of KoreaSelf-Compacting Concrete (SCC) has been widely utilized in construction projects and academic research due to its environmentally friendly components, such as fly ash and superplasticizers, which reduce water requirements. SCC’s ability to self-deposit eliminates the need for vibration, resulting in cost and energy savings. However, some experts are hesitant about its broader application due to insufficient training in modern materials. Accurately assessing construction aggregates' compressive strength (CS) ensures structural safety. Soft computing methods, which offer a cost-effective and highly accurate alternative to experimental techniques, have attracted interest in modeling dependent variables. This paper presents a novel approach by combining a Support Vector Machine (SVM) with advanced optimization algorithms to estimate the CS of SCC mixtures accurately. The significance of this approach lies in the ability of the optimization algorithms to enhance the performance of the SVM, yielding more precise predictions and addressing the limitations of traditional methods. The developed models were evaluated using several performance metrics, with results showing a strong correlation between predicted and actual values, achieving an R² of 97.3%. Furthermore, the root mean square error (RMSE) was calculated at 3.81 MPa, demonstrating the effectiveness of the proposed method in predicting SCC’s compressive strength with high accuracy.https://aeis.bilijipub.com/article_206702_a23758fe6e0474dc714cddf3a20e697a.pdfarithmetic optimization algorithmself-compacting concretegrasshopper optimization algorithmsupport vector regressioncompressive strength |
| spellingShingle | Francisca Blanco Ye Woo Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA Advances in Engineering and Intelligence Systems arithmetic optimization algorithm self-compacting concrete grasshopper optimization algorithm support vector regression compressive strength |
| title | Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA |
| title_full | Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA |
| title_fullStr | Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA |
| title_full_unstemmed | Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA |
| title_short | Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA |
| title_sort | modeling compressive strength of self compacting concrete scc using novel optimization algorithm of aoa |
| topic | arithmetic optimization algorithm self-compacting concrete grasshopper optimization algorithm support vector regression compressive strength |
| url | https://aeis.bilijipub.com/article_206702_a23758fe6e0474dc714cddf3a20e697a.pdf |
| work_keys_str_mv | AT franciscablanco modelingcompressivestrengthofselfcompactingconcretesccusingnoveloptimizationalgorithmofaoa AT yewoo modelingcompressivestrengthofselfcompactingconcretesccusingnoveloptimizationalgorithmofaoa |