Machine-learning-based models versus design-oriented models for predicting the axial compressive load of FRP-confined rectangular RC columns

To improve the prediction accuracy of axial compressive load of FRP-confined concrete columns, machinelearning 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-ofart 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.

Automated summary

Machine learning techniques were used to improve the accuracy of predicting the axial compressive load of FRP-confined concrete columns. This study reviewed influential parameters and their effects on the strength, ductility, and failure mode of FRP-strengthened columns. Machine-learning models were developed using collected datasets, and they showed good agreement with test results, outperforming existing design-oriented models with lower deviation values.