Prediction of ultimate strain and strength of CFRP-wrapped normal and high-strength concrete compressive members using ANN approach

The literature is deficit in predicting the axial strength (AS) and axial strain of carbon fiber reinforced polymer (CFRP)-wrapped normal strength concrete (NSC) and high strength concrete (HSC) compressive members using machine learning techniques. The already proposed models for predicting the AS and axial strain of CFRP-wrapped NSC compressive members were developed using a general regression analysis technique based on a small number of noisy data points by considering the limited parameters of specimens. Therefore, there is a need for a refined and accurate theoretical model for capturing the AS and axial strain of compressive members. The main objective of the current study is to develop the theoretical models for predicting the AS and axial strain of CFRP-wrapped NSC and HSC compressive members using machine learning methods. Two different analysis approaches are employed for securing the objective of the present study. The first approach is using the general regression analysis technique, and the second one is employing artificial neural networks (ANN) modeling. The testing database employed in the analysis consists of the testing results of 364 concrete compressive members subjected to compressive loading. The accuracy of the proposed empirical and ANN models is evaluated and compared on the basis of the testing results. Three statistical indices were employed to determine the performance of the currently proposed models developed in the present study. The ANN models presented the accuracy with the statistical indices R2 = 0.984, RMSE = 0.112, and MAE = 0.097 for the strength model and R2 = 0.942, RMSE = 1.211, and MAE = 0.978 for the strain model. The suggested empirical model presented the statistical indices R2 = 0.90, RMSE = 0.33, and MAE = 2.45 for the strength model and R2 = 0.80, RMSE = 2.05, and MAE = 5.34 for the strain model. The statistical evaluation showed that the ANN models are more effective and precise than the empirical ones in predicting the AS and axial strain of CFRP-wrapped circular concrete compressive members.

Automated summary

The literature lacks in predicting the axial strength and axial strain of CFRP-wrapped NSC and HSC compressive members using machine learning techniques. This study aims to develop theoretical models for predicting the strength and strain of these members. Two different analysis approaches, general regression analysis and ANN modeling, were employed and evaluated using a testing database of 364 concrete compressive members. The statistical evaluation showed that the ANN models outperformed the empirical models in predicting the axial strength and axial strain of CFRP-wrapped circular concrete compressive members.