Shear strength of steel fiber-unconfined reinforced concrete beam simulation: Application of novel intelligent model

Abstract The research promotes a new nonlinear model-based hybridized response surface method (RSM) and support vector regression (RSM-SVR) to predict shear capacity of steel fiber-reinforced concrete beams (SFRCB). Two approaches are integrated using RSM which is calibrated based on two input datasets; whereas, the SVR is calibrated based on all the predicted datasets generated by RSM. The high-cross correlation of the input dataset is provided using two nonlinear steps for modeling the SFRCB shear strength. The capacity of hybrid RSM-SVR model is validated with stand-alone intelligent models RSM, SVR and neural network (NN) in addition to eight empirical formulations. The dataset of 139 laboratory experimental tests of shear failure capacity belongs to SFRCB without stirrups, are obtained from the literature. The effects of fiber volume and the longitudinal steel ratio on the shear predictions of the normal and high-strange concrete reinforced by steel fiber are compared for the intelligent and empirical based approaches. The achieved results indicated the RSM-SVR model performed superior prediction over the comparable models. The improved agreement indexes using the RSM-SVR were improved with (0.35 and 1.9) over the empirical formulations and with (0.8, 1.2 and 3.5) over the three intelligent models of RSM, SVR and NN, respectively.

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