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AuthorWakjira, Tadesse Gemeda
AuthorEbead, Usama
AuthorAlam, M. Shahria
Available date2023-01-29T09:23:41Z
Publication Date2022
Publication NameCase Studies in Construction Materials
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.cscm.2022.e01008
URIhttp://hdl.handle.net/10576/39130
AbstractThe application of inorganic composites has proven to be an effective strengthening technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of the shear capacity of RC beams strengthened with inorganic composites has been a challenging problem due to its complex failure mechanism and the interaction between the internal and external shear reinforcements. Besides, the predictive capabilities of the existing models are not satisfactory. Thus, this research proposed machine learning (ML) based models for predicting the shear capacity of RC beams strengthened in shear with inorganic composites, for the first time, considering all important variables. The results of the analyses evidenced that the proposed ML models can be successfully used to predict the shear capacity of shear-critical RC beams strengthened with inorganic composites. Among the ML models examined herein, the extreme gradient boosting (xgBoost) model showed the highest prediction capability. The comparison among the predictions of the proposed xgBoost and existing models evidenced that the efficacy of the xgBoost model is superior to the existing models in terms of accuracy, safety, and economic aspects. Finally, reliability analysis is performed to calibrate the resistance reduction factors in order to attain target reliability indices of 3.5 and 4.0 for the proposed model. 2022 The Authors
SponsorThis paper was made possible by NPRP Grant # NPRP 13S-0209-200311 from the Qatar National Research Fund (a member of Qatar Foundation) and financial support of the Natural Sciences and Engineering Research Council (NSERC) of Canada. Open Access funding provided by the Qatar National Library. The findings achieved herein are solely the responsibility of the authors.
Languageen
PublisherElsevier
SubjectInorganic composites
Machine learning
Modeling
Reliability analysis
Retrofitting
TitleMachine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites
TypeArticle
Volume Number16
dc.accessType Open Access


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