Neural network prediction of web-crushing strength of i-shaped reinforced concrete beams

Introduction. Under the combined action of shear force and bending moment web-shear cracking takes place in the thin webs of reinforced concrete beams. The strength of the concrete struts between the cracks depends on the size of the web, concrete strength, parameters of the transverse and longitudinal reinforcement, and the shear span-to-depth ratio. Plane stress constitutive models for reinforced concrete are difficult to implement. For this reason, building codes employ empirical formulas for web-crushing strength which were obtained from the analysis of the existing experimental data. Using machine learning tools — artificial neural networks (ANN) — can serve as a solution that allows to take into account the impact of structural and loading parameters more accurately. The analyzed experimental base included the test results of 77 beams. The input layer of the ANN consisted of 7 independent variables, the output — of 1 dependent, and both linear and nonlinear functions were considered as activation functions. Materials and methods. The article studies I-shaped concrete beams with basalt fiber reinforced polymer and steel transverse reinforcement tested by authors. To ensure the statistical significance of independent variables in all indicators, the database of other authors was also considered. Neural networks were developed using STATISTICA software package. In the first stage, the input and output variables were normalized. The accuracy of the ANN model prediction was compared with the accuracy of regression models. In the last stage, the calculations were performed without normalizing the variables. Results. Artificial neural networks prediction has high accuracy. The relative error of prediction was 28.6 % for the regression method and 10.9 % for the ANN. For calculations without preliminary normalization relative error of prediction was 6.6 %. Conclusions. The results of research and other similar studies suggest ANN to be a promising tool for solving intractable problems of structural engineering.