Predicting the Shear Strength of Reinforced Concrete Beams Using Support Vector Machine - Lesmana, Cindrawaty (Jurnal Teknik Sipil, 2019)
A wide range of machine learning techniques have been successfully applied to model different civil engineering systems. The application of support vector machine (SVM) to predict the ultimate shear strengths of reinforced concrete (RC) beams with transverse reinforcements is investigated in this paper. An SVM model is built trained and tested using the available test data of 175 RC beams collected from the technical literature. The data used in the SVM model are arranged in a format of nine input parameters that cover the cylinder concrete compressive strength, yield strength of the longitudinal and transverse reinforcing bars, the shear-span-to-effective-depth ratio, the span-toeffective- depth ratio, beam’s cross-sectional dimensions, and the longitudinal and transverse reinforcement ratios. The relative performance of the SVMs shear strength predicted results were also compared to ACI building code and artificial neural network (ANNs) on the same data sets. Furthermore, the SVM shows good performance and it is proved to be competitive with ANN model and empirical solution from ACI-05.