Machine-learning-based models versus design-oriented models for predicting the axial compressive load of FRP-confined rectangular RC columns. (15th June 2023)

• Record Type: Journal Article
• Authors: Sayed, Yehia A.K.; Ibrahim, Alzhraa A.; Tamrazyan, Ashot G.; Fahmy, Mohamed F.M.
• Is Part Of: Engineering structures. Volume 285(2023)
• Publication Date: 2023-06-15
• Subjects: Rectangular RC columns -- FRP sheets -- Axial compressive strength -- Design-oriented models -- Machine learning; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2023.116030
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Conducting state-of-art review of FRP-confined rectangular concrete columns. Steel reinforcing highly affects the behavior of FRP-rectangular concrete column. Machine learning models predict the compressive load of FRP-RC rectangular columns. Machine learning models outperform the related design-oriented models. Gradient boosting and random forest machine learning models prove high efficiency. Abstract: To improve the prediction accuracy of axial compressive load of FRP-confined concrete columns, machine-learning techniques have been used recently. However, few studies have used machine learning to estimate the axial compressive load of FRP-confined rectangular RC columns. Therefore, this study introduces a state-of-art review of externally strengthened rectangular concrete columns with FRP composites: the influential parameters were introduced and their effects on the strength, ductility, and failure mode of FRP-strengthened columns were discussed. Hence, the critical design parameters were identified and used as input features in machine-learning modeling. From a practical point of view, special attention was paid to collecting any dataset related to steel reinforced rectangular concrete columns and externally confined with different types of FRP composites. These collected datasets were used to generate machine-learning models to predict the axial compressive load of rectangular RC columns confined by external FRP sheets, and were compared with existing design-oriented models. The proposed machine-learning models are found to be in good agreement with the test results in the datasets. In the comparison between the existing design-oriented models and the developed machine-learning models, the gradient boosting (GB) and random forest (RF) regressors are more accurate, and both methods achieved the lowest deviation value.