Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization
In structures with reinforced concrete walls, coupling beams join individual walls to produce a rigid assembly that withstands sideways forces. A precise forecasting of the critical shear capacity is essential to avoid early shear failure and attain the desired ductility performance of coupled shear...
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2025-01-01
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| author | Emad A. Abood Mustafa Kamal Al-Kamal Sabih Hashim Muhodir Nadia Moneem Al-Abdaly Luís Filipe Almeida Bernardo Dario De Domenico Hamza Imran |
| author_facet | Emad A. Abood Mustafa Kamal Al-Kamal Sabih Hashim Muhodir Nadia Moneem Al-Abdaly Luís Filipe Almeida Bernardo Dario De Domenico Hamza Imran |
| author_sort | Emad A. Abood |
| collection | DOAJ |
| description | In structures with reinforced concrete walls, coupling beams join individual walls to produce a rigid assembly that withstands sideways forces. A precise forecasting of the critical shear capacity is essential to avoid early shear failure and attain the desired ductility performance of coupled shear wall systems in earthquake design. This paper examines the ability of Support Vector Regression (SVR) in predicting the shear performance of coupling beams. SVR is a distinguished machine learning regression method that has been positively utilized in former works to forecast the performance of several structural members. Nevertheless, the capability of this regression method deeply relies on picking its best hyperparameters. To handle this, a heuristic optimization procedure named Particle Swarm Optimization (PSO) was merged with SVR to select the optimal hyperparameters. The data of RC coupling beams collected from the previous works were utilized to build the proposed model. Several performance metrics, including RMSE, R2, and MAE, were employed to compare the performance of the optimized model against a baseline SVR model and previous approaches. Analytical results indicate that the new optimized prediction model can assist civil engineers in designing RC coupling beam structures more effectively and outperforms existing models in predicting the shear strength of such beams. |
| format | Article |
| id | doaj-art-d519c4bfe0154d77ad5998b48ee93d32 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-d519c4bfe0154d77ad5998b48ee93d322025-01-24T13:26:07ZengMDPI AGBuildings2075-53092025-01-0115219110.3390/buildings15020191Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm OptimizationEmad A. Abood0Mustafa Kamal Al-Kamal1Sabih Hashim Muhodir2Nadia Moneem Al-Abdaly3Luís Filipe Almeida Bernardo4Dario De Domenico5Hamza Imran6Department of Material Engineering, College of Engineering, Al-Shatrah University, Al-Shatrah 64007, IraqDepartment of Civil Engineering, Al-Nahrain University, Baghdad 10081, IraqDepartment of Architectural Engineering, Cihan University Erbil, Kurdistan Region 44001, IraqConstruction and Building Engineering Technologies Department, Najaf Engineering Technical Colleges, Al-Furat-Al-Awsat Technical University, Najaf 54003, IraqGeoBioTec, Department of Civil Engineering and Architecture, University of Beira Interior, 6201-001 Covilha, PortugalDepartment of Engineering, University of Messina, Villaggio S. Agata, 98166 Messina, ItalyDepartment of Construction and Project, Al-Karkh University of Science, Baghdad 10081, IraqIn structures with reinforced concrete walls, coupling beams join individual walls to produce a rigid assembly that withstands sideways forces. A precise forecasting of the critical shear capacity is essential to avoid early shear failure and attain the desired ductility performance of coupled shear wall systems in earthquake design. This paper examines the ability of Support Vector Regression (SVR) in predicting the shear performance of coupling beams. SVR is a distinguished machine learning regression method that has been positively utilized in former works to forecast the performance of several structural members. Nevertheless, the capability of this regression method deeply relies on picking its best hyperparameters. To handle this, a heuristic optimization procedure named Particle Swarm Optimization (PSO) was merged with SVR to select the optimal hyperparameters. The data of RC coupling beams collected from the previous works were utilized to build the proposed model. Several performance metrics, including RMSE, R2, and MAE, were employed to compare the performance of the optimized model against a baseline SVR model and previous approaches. Analytical results indicate that the new optimized prediction model can assist civil engineers in designing RC coupling beam structures more effectively and outperforms existing models in predicting the shear strength of such beams.https://www.mdpi.com/2075-5309/15/2/191coupling beamsshear strengthSupport Vector RegressionParticle Swarm Optimization |
| spellingShingle | Emad A. Abood Mustafa Kamal Al-Kamal Sabih Hashim Muhodir Nadia Moneem Al-Abdaly Luís Filipe Almeida Bernardo Dario De Domenico Hamza Imran Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization Buildings coupling beams shear strength Support Vector Regression Particle Swarm Optimization |
| title | Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization |
| title_full | Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization |
| title_fullStr | Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization |
| title_full_unstemmed | Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization |
| title_short | Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization |
| title_sort | coupling beams shear capacity prediction by hybrid support vector regression and particle swarm optimization |
| topic | coupling beams shear strength Support Vector Regression Particle Swarm Optimization |
| url | https://www.mdpi.com/2075-5309/15/2/191 |
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