Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete

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dc.contributor.authorÇakıroğlu, Celal
dc.contributor.authorTusher, Tanvir Hassan
dc.contributor.authorShahjalal, Md.
dc.contributor.authorIslam, Kamrul
dc.contributor.authorBillah, A. H. M. Muntasir
dc.contributor.authorNehdi, Moncef L.
dc.date.accessioned2024-12-26T18:39:53Z
dc.date.available2024-12-26T18:39:53Z
dc.date.issued2024
dc.departmentTAÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractNovel 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.
dc.identifier.citationÇakıroğlu, C., Tusher, Tanvir H., Shahjalal, Md., Islam, K., Billah, A. H. M. M., Nehdi, Moncef L. (2024). Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete. Developments in the Built Environment, 20.
dc.identifier.doi10.1016/j.dibe.2024.100547
dc.identifier.issn2666-1659
dc.identifier.scopus2-s2.0-85204897313
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1500
dc.identifier.volume20en_US
dc.identifier.wosWOS:001327723100001
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofDevelopments in the Built Environment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectRecycled aggregateen_US
dc.subjectConcreteen_US
dc.subjectBond strengthen_US
dc.subjectFailure modeen_US
dc.subjectMachine learningen_US
dc.subjectPredictionen_US
dc.subjectGraphical user interfaceen_US
dc.titleExplainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete
dc.typeArticle

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