Bayesian optimization algorithm based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members

The use of fiber-reinforced polymer (FRP) rebars in lieu of steel rebars has led to some deviations in the shear behavior of concrete members. Several methods have been proposed to forecast the shear capacity of such members. Nonetheless, there are differences in the methods of considering the various parameters affecting shear capacity, and some of them provide widely scattered and conservative results. This paper presents a hybrid of the Bayesian optimization algorithm (BOA) and support vector regression (SVR) as a novel modeling tool for the prediction of the shear capacity of FRP-reinforced members with no stirrups. For this purpose, a large dataset of simply supported beams and unidirectional slabs reinforced with FRP were utilized. The model performance was assessed using several statistical performance indicators and compared with the Japan Society of Civil Engineers (JSCE), British Institution of Structural Engineers (BISE), Canadian Standard Association (CSA), and American Concrete Institute (ACI) design codes and guidelines, as well as some other artificial intelligence (AI) models. For development of the model, all the hyperparameters, i.e., kernel function type, epsilon, box constraint, and kernel scale, were optimized using the BOA technique. The k-fold cross validation approach was utilized to avoid overfitting of the model. It was found that the mean, median, standard deviation, minimum, maximum, and interquartile range of the developed hybrid model predictions are very close to the experimental results. The predicted results overlap the experimental data with a coefficient of determination of 95.5%. The plot of relative deviations and residual plots are scattered around the zero reference line with low deviation, which indicates that the model is reliable and valid. The error terms (e.g., mean absolute error, root mean square error) obtained for all specimens were 4.85 and 11.03, which are very low values. The correlation coefficient (R) and fractional bias (FB) were found to be 0.977 and 0.0033, which are very close to 1 and 0, respectively, thus implying a reliable prediction. The comparative investigations with other codes and guidelines show that the hybrid BOA-SVR model predictions are more accurate and robust than those of the other models. (C) 2021 Elsevier B.V. All rights reserved.