Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques
Transverse reinforcement of reinforced concrete (RC) columns contributes greatly to the ductility deformation capacity of RC structures. The existing models to predict the amount of transverse reinforcement required are all empirical models with low accuracy and large dispersion and have not conside...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2923069 |
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| _version_ | 1832552525083967488 |
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| author | Congzhen Xiao Baojuan Qiao Jianhui Li Zhiyong Yang Jiannan Ding |
| author_facet | Congzhen Xiao Baojuan Qiao Jianhui Li Zhiyong Yang Jiannan Ding |
| author_sort | Congzhen Xiao |
| collection | DOAJ |
| description | Transverse reinforcement of reinforced concrete (RC) columns contributes greatly to the ductility deformation capacity of RC structures. The existing models to predict the amount of transverse reinforcement required are all empirical models with low accuracy and large dispersion and have not considered the real ductility demand of individual components. This paper proposes a ductility design method of RC structure based on component drift ratio demand obtained from nonlinear structural dynamic analysis. To establish the best transverse reinforcement ratio prediction model for RC columns, based on an experimental database consisting of 498 columns, 12 machine learning (ML) models are trained. To solve the over-fitting problem caused by the current situation of “few samples and big errors” of the experimental database, feature engineering aiming at dimension reduction is systematically carried out through an iterative process. Through comprehensive performance evaluation on the testing set, an XGBoost model is selected. To interpret the “black box” ML model, the SHAP method and partial dependence plots are used to analyse the correlation between the input parameters and the transverse reinforcement ratio. The interpretation results are consistent with mechanical laws and engineering experience, which prove the reliability of the selected ML model. Compared with two existing empirical models, the proposed XGBoost model shows higher accuracy and smaller deviation. After safety probability analysis, the trained XGBoost model is transformed into C code and integrated into seismic design software for productive practice. An open-source data-driven model to predict the transverse reinforcement ratio required for RC columns is provided worldwide, with the flexibility to account for additional experimental results. |
| format | Article |
| id | doaj-art-bb8588f2e065462abe2f5a88313b52bf |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-bb8588f2e065462abe2f5a88313b52bf2025-02-03T05:58:31ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/2923069Prediction of Transverse Reinforcement of RC Columns Using Machine Learning TechniquesCongzhen Xiao0Baojuan Qiao1Jianhui Li2Zhiyong Yang3Jiannan Ding4China Academy of Building ResearchChina Academy of Building ResearchChina Academy of Building ResearchChina Academy of Building ResearchChina Academy of Building ResearchTransverse reinforcement of reinforced concrete (RC) columns contributes greatly to the ductility deformation capacity of RC structures. The existing models to predict the amount of transverse reinforcement required are all empirical models with low accuracy and large dispersion and have not considered the real ductility demand of individual components. This paper proposes a ductility design method of RC structure based on component drift ratio demand obtained from nonlinear structural dynamic analysis. To establish the best transverse reinforcement ratio prediction model for RC columns, based on an experimental database consisting of 498 columns, 12 machine learning (ML) models are trained. To solve the over-fitting problem caused by the current situation of “few samples and big errors” of the experimental database, feature engineering aiming at dimension reduction is systematically carried out through an iterative process. Through comprehensive performance evaluation on the testing set, an XGBoost model is selected. To interpret the “black box” ML model, the SHAP method and partial dependence plots are used to analyse the correlation between the input parameters and the transverse reinforcement ratio. The interpretation results are consistent with mechanical laws and engineering experience, which prove the reliability of the selected ML model. Compared with two existing empirical models, the proposed XGBoost model shows higher accuracy and smaller deviation. After safety probability analysis, the trained XGBoost model is transformed into C code and integrated into seismic design software for productive practice. An open-source data-driven model to predict the transverse reinforcement ratio required for RC columns is provided worldwide, with the flexibility to account for additional experimental results.http://dx.doi.org/10.1155/2022/2923069 |
| spellingShingle | Congzhen Xiao Baojuan Qiao Jianhui Li Zhiyong Yang Jiannan Ding Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques Advances in Civil Engineering |
| title | Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques |
| title_full | Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques |
| title_fullStr | Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques |
| title_full_unstemmed | Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques |
| title_short | Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques |
| title_sort | prediction of transverse reinforcement of rc columns using machine learning techniques |
| url | http://dx.doi.org/10.1155/2022/2923069 |
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