Explainable prediction model for punching shear strength of FRP-RC slabs based on kernel density estimation and XGBoost
Abstract Reinforced concrete (RC) slabs are widely used in modern building structures due to their superior properties and ease of construction. However, their mechanical properties are limited by their punching shear strength in the connection region with the columns. Researchers have attempted to...
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Nature Portfolio
2024-12-01
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| Online Access: | https://doi.org/10.1038/s41598-024-82159-3 |
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| author | Sheng Zheng Tianyu Hu Nima Khodadadi Antonio Nanni |
| author_facet | Sheng Zheng Tianyu Hu Nima Khodadadi Antonio Nanni |
| author_sort | Sheng Zheng |
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| description | Abstract Reinforced concrete (RC) slabs are widely used in modern building structures due to their superior properties and ease of construction. However, their mechanical properties are limited by their punching shear strength in the connection region with the columns. Researchers have attempted to add steel reinforcement in the form of studs and randomly distributed fibers to concrete slabs to improve the punching strength. An additional strengthening method that consists of the application is a Fiber-Reinforced Polymer (FRP). However, current codes poorly calculate the punching shear strength of FRP-RC slabs. The aim of this study is to create a robust model that can accurately predict its punching shear strength, thus improving the analysis and design of composite structures with FRP-RC slabs. In this study, 189 sets of experimental data were collected and expanded using kernel density estimation (KDE), considering the small amount of data. Secondly, a punching shear strength prediction model for FRP-RC panels was constructed using XGBoost and compared with the model modeled by codes and researchers. Finally, a model explainability study was conducted using SHapley additive exPlanations (SHAP). The results show that kernel density estimation significantly improves the robustness and accuracy of XGBoost. The R-squared, standard deviation, and root mean square error of XGBoost on the training set are 0.99, 0.001, and 0.001, respectively. On the test set, the R-squared, standard deviation, and root mean square error are 0.96, 62.687, and 67.484, respectively. The effective depth of the FRP-RC slabs is the most important and proportional to the punching shear strength. This study can provide guidance for the design of FRP-RC slabs. |
| format | Article |
| id | doaj-art-e05d5d7c705849a28a33a7b47164b278 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e05d5d7c705849a28a33a7b47164b2782025-01-05T12:28:05ZengNature PortfolioScientific Reports2045-23222024-12-0114111510.1038/s41598-024-82159-3Explainable prediction model for punching shear strength of FRP-RC slabs based on kernel density estimation and XGBoostSheng Zheng0Tianyu Hu1Nima Khodadadi2Antonio Nanni3Shanghai Urban Construction Vocational CollegeState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong UniversityDepartment of Civil and Architectural Engineering, University of MiamiDepartment of Civil and Architectural Engineering, University of MiamiAbstract Reinforced concrete (RC) slabs are widely used in modern building structures due to their superior properties and ease of construction. However, their mechanical properties are limited by their punching shear strength in the connection region with the columns. Researchers have attempted to add steel reinforcement in the form of studs and randomly distributed fibers to concrete slabs to improve the punching strength. An additional strengthening method that consists of the application is a Fiber-Reinforced Polymer (FRP). However, current codes poorly calculate the punching shear strength of FRP-RC slabs. The aim of this study is to create a robust model that can accurately predict its punching shear strength, thus improving the analysis and design of composite structures with FRP-RC slabs. In this study, 189 sets of experimental data were collected and expanded using kernel density estimation (KDE), considering the small amount of data. Secondly, a punching shear strength prediction model for FRP-RC panels was constructed using XGBoost and compared with the model modeled by codes and researchers. Finally, a model explainability study was conducted using SHapley additive exPlanations (SHAP). The results show that kernel density estimation significantly improves the robustness and accuracy of XGBoost. The R-squared, standard deviation, and root mean square error of XGBoost on the training set are 0.99, 0.001, and 0.001, respectively. On the test set, the R-squared, standard deviation, and root mean square error are 0.96, 62.687, and 67.484, respectively. The effective depth of the FRP-RC slabs is the most important and proportional to the punching shear strength. This study can provide guidance for the design of FRP-RC slabs.https://doi.org/10.1038/s41598-024-82159-3Fiber-reinforced polymerRC slabsKernel density estimationXGBoostSHapley additive exPlanations |
| spellingShingle | Sheng Zheng Tianyu Hu Nima Khodadadi Antonio Nanni Explainable prediction model for punching shear strength of FRP-RC slabs based on kernel density estimation and XGBoost Scientific Reports Fiber-reinforced polymer RC slabs Kernel density estimation XGBoost SHapley additive exPlanations |
| title | Explainable prediction model for punching shear strength of FRP-RC slabs based on kernel density estimation and XGBoost |
| title_full | Explainable prediction model for punching shear strength of FRP-RC slabs based on kernel density estimation and XGBoost |
| title_fullStr | Explainable prediction model for punching shear strength of FRP-RC slabs based on kernel density estimation and XGBoost |
| title_full_unstemmed | Explainable prediction model for punching shear strength of FRP-RC slabs based on kernel density estimation and XGBoost |
| title_short | Explainable prediction model for punching shear strength of FRP-RC slabs based on kernel density estimation and XGBoost |
| title_sort | explainable prediction model for punching shear strength of frp rc slabs based on kernel density estimation and xgboost |
| topic | Fiber-reinforced polymer RC slabs Kernel density estimation XGBoost SHapley additive exPlanations |
| url | https://doi.org/10.1038/s41598-024-82159-3 |
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