Prediction of moment improvement in UHPC strengthened damaged RC beams based on data augmented machine learning
Strengthening of damaged reinforced concrete (RC) structures with ultra high performance concrete (UHPC) can increase their load carrying capacity and durability. However, there are limited studies that forecast the moment improvement (Mu) in strengthening damaged RC beams. The aim of this study is...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525007375 |
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| _version_ | 1849422458415742976 |
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| author | Weidong Xu Decheng Ji Yong Yu Xianying Shi |
| author_facet | Weidong Xu Decheng Ji Yong Yu Xianying Shi |
| author_sort | Weidong Xu |
| collection | DOAJ |
| description | Strengthening of damaged reinforced concrete (RC) structures with ultra high performance concrete (UHPC) can increase their load carrying capacity and durability. However, there are limited studies that forecast the moment improvement (Mu) in strengthening damaged RC beams. The aim of this study is to develop a reliable model that can precisely predict Mu. Initially, the researchers gathered 173 datasets from experimental studies. Due to the limited amount of data available, kernel density estimation (KDE) was employed to expand the data. Subsequently, six machine learning algorithms were developed to predict the Mu. In addition, a new prediction model was constructed by considering the failure modes of the strengthened beams. Finally, Shapley Additive Explanations were employed to conduct an evaluation of model explainability. The results show that KDE can improve the robustness and accuracy of the model. Extreme gradient boosting performed best in predicting Mu and considering the failure mode could improve the accuracy of the model. The height of the RC beam, the reinforcement ratio of the UHPC, and the width of the RC beam have a large and proportional effect on Mu. This study can provide guidance for the engineering design of UHPC strengthened damaged RC beams. |
| format | Article |
| id | doaj-art-c6facfd116a9476683d8182bee8640fa |
| institution | Kabale University |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-c6facfd116a9476683d8182bee8640fa2025-08-20T03:31:06ZengElsevierCase Studies in Construction Materials2214-50952025-12-0123e0493910.1016/j.cscm.2025.e04939Prediction of moment improvement in UHPC strengthened damaged RC beams based on data augmented machine learningWeidong Xu0Decheng Ji1Yong Yu2Xianying Shi3College of Ocean and Civil Engineering, Dalian Ocean University, Dalian 116023, ChinaCollege of Ocean and Civil Engineering, Dalian Ocean University, Dalian 116023, ChinaSchool of Future Transportation, Guangzhou Maritime University, Guangzhou 510725, China; Corresponding authors.College of Ocean and Civil Engineering, Dalian Ocean University, Dalian 116023, China; Corresponding authors.Strengthening of damaged reinforced concrete (RC) structures with ultra high performance concrete (UHPC) can increase their load carrying capacity and durability. However, there are limited studies that forecast the moment improvement (Mu) in strengthening damaged RC beams. The aim of this study is to develop a reliable model that can precisely predict Mu. Initially, the researchers gathered 173 datasets from experimental studies. Due to the limited amount of data available, kernel density estimation (KDE) was employed to expand the data. Subsequently, six machine learning algorithms were developed to predict the Mu. In addition, a new prediction model was constructed by considering the failure modes of the strengthened beams. Finally, Shapley Additive Explanations were employed to conduct an evaluation of model explainability. The results show that KDE can improve the robustness and accuracy of the model. Extreme gradient boosting performed best in predicting Mu and considering the failure mode could improve the accuracy of the model. The height of the RC beam, the reinforcement ratio of the UHPC, and the width of the RC beam have a large and proportional effect on Mu. This study can provide guidance for the engineering design of UHPC strengthened damaged RC beams.http://www.sciencedirect.com/science/article/pii/S2214509525007375Reinforced concrete structuresUltra high performance concreteMoment ImprovementMachine learningShapley additive explanations |
| spellingShingle | Weidong Xu Decheng Ji Yong Yu Xianying Shi Prediction of moment improvement in UHPC strengthened damaged RC beams based on data augmented machine learning Case Studies in Construction Materials Reinforced concrete structures Ultra high performance concrete Moment Improvement Machine learning Shapley additive explanations |
| title | Prediction of moment improvement in UHPC strengthened damaged RC beams based on data augmented machine learning |
| title_full | Prediction of moment improvement in UHPC strengthened damaged RC beams based on data augmented machine learning |
| title_fullStr | Prediction of moment improvement in UHPC strengthened damaged RC beams based on data augmented machine learning |
| title_full_unstemmed | Prediction of moment improvement in UHPC strengthened damaged RC beams based on data augmented machine learning |
| title_short | Prediction of moment improvement in UHPC strengthened damaged RC beams based on data augmented machine learning |
| title_sort | prediction of moment improvement in uhpc strengthened damaged rc beams based on data augmented machine learning |
| topic | Reinforced concrete structures Ultra high performance concrete Moment Improvement Machine learning Shapley additive explanations |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525007375 |
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