Prediction of Catenary Action Capacity of RC Beam-Column Substructures under a Missing Column Scenario Using Evolutionary Algorithm
Catenary action plays crucial role in resisting the applied vertical load at large deformations stage in reinforced concrete (RC) structures. This paper aims to predict the catenary action capacity of RC beam-column substructures by utilizing the distinctive properties of gene expression programming...
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| Published in: | KSCE Journal of Civil Engineering Vol. 25; no. 3; pp. 891 - 905 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Seoul
Korean Society of Civil Engineers
01.03.2021
Springer Nature B.V 대한토목학회 |
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| ISSN: | 1226-7988, 1976-3808 |
| Online Access: | Get full text |
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| Abstract | Catenary action plays crucial role in resisting the applied vertical load at large deformations stage in reinforced concrete (RC) structures. This paper aims to predict the catenary action capacity of RC beam-column substructures by utilizing the distinctive properties of gene expression programming (GEP). The input parameters selected for the modelling are: double-beam span-to-depth ratio, relative axial restraints stiffness, relative rotational restraints stiffness, bottom and top longitudinal reinforcement ratios, and yield strength of longitudinal rebars. A comprehensive and reliable database was collated from internationally published research articles to develop and verify the model. The GEP-based model was assessed by comparing its performance with regression based model. Various statistical indicators and external validation criteria suggested in literature proved that the model is accurate and possess high prediction and generalization capacity. Sensitivity analysis was carried out to show the contributions of the input parameters, while parametric analysis was performed to show that the proposed model is not merely a combination of the input parameters but can accurately represent the given physical system. The proposed formulation from GEP is found to be simple, robust, and easy to utilize for pre-design purposes. |
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| AbstractList | Catenary action plays crucial role in resisting the applied vertical load at large deformations stage in reinforced concrete (RC) structures. This paper aims to predict the catenary action capacity of RC beam-column substructures by utilizing the distinctive properties of gene expression programming (GEP). The input parameters selected for the modelling are: double-beam span-to-depth ratio, relative axial restraints stiffness, relative rotational restraints stiffness, bottom and top longitudinal reinforcement ratios, and yield strength of longitudinal rebars. A comprehensive and reliable database was collated from internationally published research articles to develop and verify the model. The GEP-based model was assessed by comparing its performance with regression based model. Various statistical indicators and external validation criteria suggested in literature proved that the model is accurate and possess high prediction and generalization capacity. Sensitivity analysis was carried out to show the contributions of the input parameters, while parametric analysis was performed to show that the proposed model is not merely a combination of the input parameters but can accurately represent the given physical system. The proposed formulation from GEP is found to be simple, robust, and easy to utilize for pre-design purposes. KCI Citation Count: 52 Catenary action plays crucial role in resisting the applied vertical load at large deformations stage in reinforced concrete (RC) structures. This paper aims to predict the catenary action capacity of RC beam-column substructures by utilizing the distinctive properties of gene expression programming (GEP). The input parameters selected for the modelling are: double-beam span-to-depth ratio, relative axial restraints stiffness, relative rotational restraints stiffness, bottom and top longitudinal reinforcement ratios, and yield strength of longitudinal rebars. A comprehensive and reliable database was collated from internationally published research articles to develop and verify the model. The GEP-based model was assessed by comparing its performance with regression based model. Various statistical indicators and external validation criteria suggested in literature proved that the model is accurate and possess high prediction and generalization capacity. Sensitivity analysis was carried out to show the contributions of the input parameters, while parametric analysis was performed to show that the proposed model is not merely a combination of the input parameters but can accurately represent the given physical system. The proposed formulation from GEP is found to be simple, robust, and easy to utilize for pre-design purposes. |
| Author | Iqbal, Muhammad Farjad Javed, Muhammad Faisal Yang, Jian Wang, Feiliang Liu, Qing-feng Azim, Iftikhar Mahmood, Zafar |
| Author_xml | – sequence: 1 givenname: Iftikhar orcidid: 0000-0001-6082-5643 surname: Azim fullname: Azim, Iftikhar organization: Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University – sequence: 2 givenname: Jian surname: Yang fullname: Yang, Jian email: j.yang.1@sjtu.edu.cn organization: Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University – sequence: 3 givenname: Muhammad Farjad orcidid: 0000-0002-5985-0902 surname: Iqbal fullname: Iqbal, Muhammad Farjad organization: Dept. of Civil Engineering, GIK Institute of Engineering Sciences and Technology – sequence: 4 givenname: Zafar surname: Mahmood fullname: Mahmood, Zafar organization: Dept. of Maths, Stats & Computer Science, The University of Agriculture – sequence: 5 givenname: Muhammad Faisal surname: Javed fullname: Javed, Muhammad Faisal organization: Dept. of Civil Engineering, COMSATS University Islamabad – sequence: 6 givenname: Feiliang surname: Wang fullname: Wang, Feiliang organization: Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University – sequence: 7 givenname: Qing-feng surname: Liu fullname: Liu, Qing-feng organization: Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University |
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| Copyright | Korean Society of Civil Engineers 2021 Korean Society of Civil Engineers 2021. |
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| Keywords | Progressive collapse Regression analysis RC beam-column substructure Catenary action Gene expression programming |
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| SubjectTerms | Beam-columns Catenaries Catenary Chromosomes Civil Engineering Constraints Engineering Evolutionary algorithms Gene expression Genetic algorithms Geotechnical Engineering & Applied Earth Sciences Industrial Pollution Prevention Load Mathematical models Mutation Naval engineering Parameters Parametric analysis Regression analysis Regression models Reinforced concrete Sensitivity analysis Statistical analysis Stiffness Structural Engineering Vertical loads Yield stress 토목공학 |
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| Title | Prediction of Catenary Action Capacity of RC Beam-Column Substructures under a Missing Column Scenario Using Evolutionary Algorithm |
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