Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams
Glass fiber-reinforced polymer (GFRP)-reinforced concrete (RC) flexural elements are typically designed to fulfill the serviceability criteria, including deflection and crack width. Crack width control in RC structures enhances durability, increases service life, and improves aesthetic appearance. T...
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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/S2214509525008034 |
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| author | Omid Habibi Omar Gouda Khaled Galal |
| author_facet | Omid Habibi Omar Gouda Khaled Galal |
| author_sort | Omid Habibi |
| collection | DOAJ |
| description | Glass fiber-reinforced polymer (GFRP)-reinforced concrete (RC) flexural elements are typically designed to fulfill the serviceability criteria, including deflection and crack width. Crack width control in RC structures enhances durability, increases service life, and improves aesthetic appearance. The design provisions of ACI 440.11–22 and CSA S806–12 incorporate a bond-dependent coefficient, kb, into equations controlling crack width to account for the bond between GFRP bars and concrete. The primary objective of this research is to apply eight machine learning (ML) models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), K nearest neighbors (KNN), Ridge, and Lasso, to enhance crack width prediction accuracy in GFRP-RC beams. The study also considers the importance of the kb coefficient in estimating the crack width and investigates the key factors that primarily impact its value. The results indicated that existing design provisions generally overestimate crack width, whereas ML models demonstrate a noticeably closer alignment with experimental data. Additionally, AdaBoost stands out as the most accurate predictor of crack width. SHapley Additive exPlanations (SHAP) analysis highlights that GFRP bar strain, bar spacing, and concrete cover significantly impact the kb coefficient. |
| format | Article |
| id | doaj-art-e94736c607a94975b5db3b4154e776d1 |
| institution | DOAJ |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-e94736c607a94975b5db3b4154e776d12025-08-20T02:41:17ZengElsevierCase Studies in Construction Materials2214-50952025-12-0123e0500510.1016/j.cscm.2025.e05005Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beamsOmid Habibi0Omar Gouda1Khaled Galal2Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec H3G 2W1, CanadaDepartment of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec H3G 2W1, CanadaCorresponding author.; Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec H3G 2W1, CanadaGlass fiber-reinforced polymer (GFRP)-reinforced concrete (RC) flexural elements are typically designed to fulfill the serviceability criteria, including deflection and crack width. Crack width control in RC structures enhances durability, increases service life, and improves aesthetic appearance. The design provisions of ACI 440.11–22 and CSA S806–12 incorporate a bond-dependent coefficient, kb, into equations controlling crack width to account for the bond between GFRP bars and concrete. The primary objective of this research is to apply eight machine learning (ML) models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), K nearest neighbors (KNN), Ridge, and Lasso, to enhance crack width prediction accuracy in GFRP-RC beams. The study also considers the importance of the kb coefficient in estimating the crack width and investigates the key factors that primarily impact its value. The results indicated that existing design provisions generally overestimate crack width, whereas ML models demonstrate a noticeably closer alignment with experimental data. Additionally, AdaBoost stands out as the most accurate predictor of crack width. SHapley Additive exPlanations (SHAP) analysis highlights that GFRP bar strain, bar spacing, and concrete cover significantly impact the kb coefficient.http://www.sciencedirect.com/science/article/pii/S2214509525008034GFRPRC structuresCrack widthBond-dependent coefficientMachine learningSHAP |
| spellingShingle | Omid Habibi Omar Gouda Khaled Galal Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams Case Studies in Construction Materials GFRP RC structures Crack width Bond-dependent coefficient Machine learning SHAP |
| title | Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams |
| title_full | Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams |
| title_fullStr | Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams |
| title_full_unstemmed | Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams |
| title_short | Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams |
| title_sort | machine learning based prediction of crack width and bond dependent coefficient kb in gfrp reinforced concrete beams |
| topic | GFRP RC structures Crack width Bond-dependent coefficient Machine learning SHAP |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525008034 |
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