Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings
The estimation of lateral strength in reinforced concrete (RC) columns subjected to cyclic loads is crucial in structural design. The failure of RC columns under lateral forces can lead to catastrophic structural collapses. This fact emphasizes the need for accurate assessments of their dynamic beha...
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302401380X |
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| author | Phu-Anh-Huy Pham Nhat-Duc Hoang |
| author_facet | Phu-Anh-Huy Pham Nhat-Duc Hoang |
| author_sort | Phu-Anh-Huy Pham |
| collection | DOAJ |
| description | The estimation of lateral strength in reinforced concrete (RC) columns subjected to cyclic loads is crucial in structural design. The failure of RC columns under lateral forces can lead to catastrophic structural collapses. This fact emphasizes the need for accurate assessments of their dynamic behavior. This paper proposes a data-driven model for estimating the lateral strength of RC columns. A historical dataset comprising 12 predictor variables and 247 samples has been compiled to train and validate of the proposed approach. The extreme gradient boosting machine (XGBoost) is employed to establish a predictive relationship between the lateral strength of RC columns and their influencing factors. Since model selection is critical for constructing a reliable prediction mode, this study relies on utilizing metaheuristic approaches, including Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, and Ant Colony Optimization, to optimize the performance of the XGBoost model. Experimental results show that the integration of Ant Colony Optimization and XGBoost can help attain outstanding prediction accuracy with a correlation of determination (R2) of 0.95. Additionally, an asymmetric squared error loss function is utilized to reduce overestimations by 12 %. The newly developed method can be utilized in practical applications where reliable predictions of the lateral strength of RC columns under cyclic loads are required. |
| format | Article |
| id | doaj-art-bfe3ea5d3d7047beb54e9d32b9ee30b8 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-bfe3ea5d3d7047beb54e9d32b9ee30b82025-08-20T02:34:35ZengElsevierResults in Engineering2590-12302024-12-012410312510.1016/j.rineng.2024.103125Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadingsPhu-Anh-Huy Pham0Nhat-Duc Hoang1Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Viet Nam; Corresponding author. 03 Quang Trung, Danang, Viet Nam.Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Viet NamThe estimation of lateral strength in reinforced concrete (RC) columns subjected to cyclic loads is crucial in structural design. The failure of RC columns under lateral forces can lead to catastrophic structural collapses. This fact emphasizes the need for accurate assessments of their dynamic behavior. This paper proposes a data-driven model for estimating the lateral strength of RC columns. A historical dataset comprising 12 predictor variables and 247 samples has been compiled to train and validate of the proposed approach. The extreme gradient boosting machine (XGBoost) is employed to establish a predictive relationship between the lateral strength of RC columns and their influencing factors. Since model selection is critical for constructing a reliable prediction mode, this study relies on utilizing metaheuristic approaches, including Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, and Ant Colony Optimization, to optimize the performance of the XGBoost model. Experimental results show that the integration of Ant Colony Optimization and XGBoost can help attain outstanding prediction accuracy with a correlation of determination (R2) of 0.95. Additionally, an asymmetric squared error loss function is utilized to reduce overestimations by 12 %. The newly developed method can be utilized in practical applications where reliable predictions of the lateral strength of RC columns under cyclic loads are required.http://www.sciencedirect.com/science/article/pii/S259012302401380XLateral strengthShear strengthReinforced concrete columnsGradient boostingMetaheuristic |
| spellingShingle | Phu-Anh-Huy Pham Nhat-Duc Hoang Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings Results in Engineering Lateral strength Shear strength Reinforced concrete columns Gradient boosting Metaheuristic |
| title | Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings |
| title_full | Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings |
| title_fullStr | Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings |
| title_full_unstemmed | Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings |
| title_short | Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings |
| title_sort | metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings |
| topic | Lateral strength Shear strength Reinforced concrete columns Gradient boosting Metaheuristic |
| url | http://www.sciencedirect.com/science/article/pii/S259012302401380X |
| work_keys_str_mv | AT phuanhhuypham metaheuristicoptimizationofextremegradientboostingmachineforenhancedpredictionoflateralstrengthofreinforcedconcretecolumnsundercyclicloadings AT nhatduchoang metaheuristicoptimizationofextremegradientboostingmachineforenhancedpredictionoflateralstrengthofreinforcedconcretecolumnsundercyclicloadings |