Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading
To meet the growing need for resilient structures in seismic and high-impact zones, accurate prediction of the response of reinforced concrete (RC) beams under impact loads is essential. Traditional methods, such as experimental testing and high fidelity finite element models, are often time consumi...
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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/S2590123024013902 |
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| author | Ali Husnain Munir Iqbal Hafiz Ahmed Waqas Mohammed El-Meligy Muhammad Faisal Javed Rizwan Ullah |
| author_facet | Ali Husnain Munir Iqbal Hafiz Ahmed Waqas Mohammed El-Meligy Muhammad Faisal Javed Rizwan Ullah |
| author_sort | Ali Husnain |
| collection | DOAJ |
| description | To meet the growing need for resilient structures in seismic and high-impact zones, accurate prediction of the response of reinforced concrete (RC) beams under impact loads is essential. Traditional methods, such as experimental testing and high fidelity finite element models, are often time consuming and resource intensive. To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques—including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network—for predicting the peak response of RC beams under impact loads. A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. Except for KNN, all models showed satisfactory generalization capabilities with R2 values over 0.8. Statistical errors such as RMSE, a-10 index, MAE, and a-20 index are within acceptable limits. The GPR model is the most effective with R2 value of 0.95. Moreover, Shapely analysis identified beam depth, impact velocity, and beam breadth as critical factors. Overall, this study demonstrates the efficacy of machine learning in accurately predicting the behavior of RC structures under impact loads, providing valuable tools for civil engineers in design and analysis. |
| format | Article |
| id | doaj-art-ad98f13bd823468dae67d9d9faf6f075 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-ad98f13bd823468dae67d9d9faf6f0752024-12-19T10:58:18ZengElsevierResults in Engineering2590-12302024-12-0124103135Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loadingAli Husnain0Munir Iqbal1Hafiz Ahmed Waqas2Mohammed El-Meligy3Muhammad Faisal Javed4Rizwan Ullah5Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, Khyber Pakhtunkhwa, 23640, PakistanDepartment of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, Khyber Pakhtunkhwa, 23640, Pakistan; Corresponding author.Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, Khyber Pakhtunkhwa, 23640, PakistanApplied Science Research Center, Applied Science Private University, Amman, Jordan; Jadara University Research Center, Jadara University, PO Box 733, Irbid, JordanDepartment of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, Khyber Pakhtunkhwa, 23640, Pakistan; Western Caspian University, Baku, Azerbaijan; Corresponding author.Department of Civil Engineering, Comsats University Islamabad-Abbottabad Campus, PakistanTo meet the growing need for resilient structures in seismic and high-impact zones, accurate prediction of the response of reinforced concrete (RC) beams under impact loads is essential. Traditional methods, such as experimental testing and high fidelity finite element models, are often time consuming and resource intensive. To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques—including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network—for predicting the peak response of RC beams under impact loads. A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. Except for KNN, all models showed satisfactory generalization capabilities with R2 values over 0.8. Statistical errors such as RMSE, a-10 index, MAE, and a-20 index are within acceptable limits. The GPR model is the most effective with R2 value of 0.95. Moreover, Shapely analysis identified beam depth, impact velocity, and beam breadth as critical factors. Overall, this study demonstrates the efficacy of machine learning in accurately predicting the behavior of RC structures under impact loads, providing valuable tools for civil engineers in design and analysis.http://www.sciencedirect.com/science/article/pii/S2590123024013902Machine learningPeak responseGene expression programmingShapley |
| spellingShingle | Ali Husnain Munir Iqbal Hafiz Ahmed Waqas Mohammed El-Meligy Muhammad Faisal Javed Rizwan Ullah Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading Results in Engineering Machine learning Peak response Gene expression programming Shapley |
| title | Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading |
| title_full | Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading |
| title_fullStr | Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading |
| title_full_unstemmed | Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading |
| title_short | Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading |
| title_sort | machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading |
| topic | Machine learning Peak response Gene expression programming Shapley |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024013902 |
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