Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete
Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Developments in the Built Environment |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266616592400228X |
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| author | Celal Cakiroglu Tanvir Hassan Tusher Md. Shahjalal Kamrul Islam AHM Muntasir Billah Moncef L. Nehdi |
| author_facet | Celal Cakiroglu Tanvir Hassan Tusher Md. Shahjalal Kamrul Islam AHM Muntasir Billah Moncef L. Nehdi |
| author_sort | Celal Cakiroglu |
| collection | DOAJ |
| description | Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension of the bond failure pattern. Considering the increasing significance of sustainable construction methods, it is crucial to examine both the failure pattern and bond strength to expand the versatility of RCA in various reinforced concrete structures. Accordingly, XGBoost, CatBoost, Random Forest, and LightGBM were trained for this purpose. Model performance was appraised using various statistical metrics, while failure classification performance was assessed using accuracy, recall, and precision indicators. Model performance was ranked using Copeland's algorithm. Feature importance was quantified using SHAP. Coefficient of determination of 0.91 was achieved by XGBoost in predicting bond strength, outperforming other nine analytical models in literature. Failure mode was predicted with accuracy of 94% by CatBoost, XGBoost, and LightGBM. Embedment length and compressive strength features had greatest influence on bond strength and failure mode, respectively. User-friendly graphical interface was developed to harvest ML models in real-world engineering practice. Online free access accurately assigns to any given combination of input features corresponding accurate rebar bond strength and failure mode. |
| format | Article |
| id | doaj-art-359e50ecabd8487ebfd86fd16bd1961a |
| institution | OA Journals |
| issn | 2666-1659 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developments in the Built Environment |
| spelling | doaj-art-359e50ecabd8487ebfd86fd16bd1961a2025-08-20T01:58:22ZengElsevierDevelopments in the Built Environment2666-16592024-12-012010054710.1016/j.dibe.2024.100547Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concreteCelal Cakiroglu0Tanvir Hassan Tusher1Md. Shahjalal2Kamrul Islam3AHM Muntasir Billah4Moncef L. Nehdi5Department of Civil Engineering, Turkish-German University, Istanbul, TurkeyDepartment of Civil Engineering, University of Calgary, Calgary, CanadaDepartment of Civil Engineering, University of Calgary, Calgary, CanadaDepartment of Civil, Geological and Mining Engineering, Ecole Polytechnique de Montreal, CanadaDepartment of Civil Engineering, University of Calgary, Calgary, CanadaCollege of Engineering and Physical Sciences, University of Guelph, Guelph, ON, Canada; Corresponding author.Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension of the bond failure pattern. Considering the increasing significance of sustainable construction methods, it is crucial to examine both the failure pattern and bond strength to expand the versatility of RCA in various reinforced concrete structures. Accordingly, XGBoost, CatBoost, Random Forest, and LightGBM were trained for this purpose. Model performance was appraised using various statistical metrics, while failure classification performance was assessed using accuracy, recall, and precision indicators. Model performance was ranked using Copeland's algorithm. Feature importance was quantified using SHAP. Coefficient of determination of 0.91 was achieved by XGBoost in predicting bond strength, outperforming other nine analytical models in literature. Failure mode was predicted with accuracy of 94% by CatBoost, XGBoost, and LightGBM. Embedment length and compressive strength features had greatest influence on bond strength and failure mode, respectively. User-friendly graphical interface was developed to harvest ML models in real-world engineering practice. Online free access accurately assigns to any given combination of input features corresponding accurate rebar bond strength and failure mode.http://www.sciencedirect.com/science/article/pii/S266616592400228XRecycled aggregateConcreteBond strengthFailure modeMachine learningPrediction |
| spellingShingle | Celal Cakiroglu Tanvir Hassan Tusher Md. Shahjalal Kamrul Islam AHM Muntasir Billah Moncef L. Nehdi Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete Developments in the Built Environment Recycled aggregate Concrete Bond strength Failure mode Machine learning Prediction |
| title | Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete |
| title_full | Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete |
| title_fullStr | Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete |
| title_full_unstemmed | Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete |
| title_short | Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete |
| title_sort | explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete |
| topic | Recycled aggregate Concrete Bond strength Failure mode Machine learning Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S266616592400228X |
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