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المؤلفAbushanab, Abdelrahman
المؤلفWakjira, Tadesse Gemeda
المؤلفAlnahhal, Wael
تاريخ الإتاحة2024-08-08T05:23:24Z
تاريخ النشر2023
اسم المنشورSustainability (Switzerland)
المصدرScopus
الرقم المعياري الدولي للكتاب20711050
معرّف المصادر الموحدhttp://dx.doi.org/10.3390/su15064824
معرّف المصادر الموحدhttp://hdl.handle.net/10576/57514
الملخصSteel corrosion poses a serious threat to the structural performance of reinforced concrete (RC) structures. Thus, this study evaluates the flexural capacity of RC beams through machine learning (ML)-based techniques with six parameters used as input features: beam width, beam effective depth, concrete compressive strength, reinforcement ratio, reinforcement yield strength, and corrosion level. Four single and ensemble ML models are evaluated; namely, decision tree, support vector machine, adaptive boosting, and gradient boosting. Hyperparameters of each model were optimized using grid search and K-fold cross-validation with root mean squared error used as the performance index. The predictive performance of each model was assessed using four statistical performance metrics. The analysis results demonstrated that the decision tree model exhibited overfitting and limited generalization ability. The adaptive boosting model also had a slight overfitting issue. In addition, the support vector machine reported comparable accuracy to that of adaptive boosting. Conversely, the proposed gradient boosting ensemble model achieved the best performance with strong generalization ability, as indicated by its lowest mean absolute error of 2.78 kN.m, mean absolute percent error of 13.40%, and root mean squared error of 3.56 kN.m, and the highest coefficient of determination of 97.30% on the test dataset. The optimized gradient boosting model has been deployed into a graphical user interface, allowing for practical implementation of the model and enabling fast, efficient, and intelligent prediction of the flexural capacity of corroded RC beams.
راعي المشروعThis publication was made possible by GSRA grant GSRA6-1-0509-19022 from the Qatar National Research Fund (QNRF, a member of Qatar Foundation).
اللغةen
الناشرMultidisciplinary Digital Publishing Institute (MDPI)
الموضوعartificial intelligence
beams
corrosion
flexural capacity
GUI
machine learning
العنوانMachine Learning-Based Flexural Capacity Prediction of Corroded RC Beams with an Efficient and User-Friendly Tool
النوعArticle
رقم العدد6
رقم المجلد15
dc.accessType Open Access


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