LAUSR

Abstract This paper presents the use Machine Learning (ML) techniques to study the behavior of shear-deficient reinforced concrete (RC) beams strengthened in shear with side-bonded and U-wrapped fiber-reinforced polymers (FRP) laminates. An extensive database consisting of 120 tested specimen and 15 parameters was collected. The resilient back-propagating neural network (RBPNN) was used as a regression tool and the recursive feature elimination (RFE) algorithm and neural interpretation diagram (NID) were employed within the validated RBPNN to identify the parameters that greatly influence the prediction of FRP shear capacity. The results indicated that the RBPNN with the selected parameters was capable of predicting the FRP shear capacity more accurately (r2 = 0.885; RMSE = 8.1 kN) than that of the RBPNN with the original 15 parameters (r2 = 0.668; RMSE = 16.6 kN). The model also outperformed previously established standard predictions of ACI 440.R-17, fib14 and CNRDT200. A comprehensive parametric study was conducted and it concluded that the implementation of RBPNN with RFE and NID, separately, is a viable tool for assessing the strength and behavior of FRP in shear strengthened beams.