Prediction and feature analysis of punching shear strength of two-way reinforced concrete slabs using optimized machine learning algorithm and Shapley additive explanations

Abstract

Punching shear strength (PSS) is an important index for the design and analysis of two-way reinforced concrete slabs. To accurately predict the PSS of two-way reinforced concrete slabs, a hybrid PSO-SVR model, which is the combination of support vector regression (SVR) and particle swarm optimization (PSO) algorithm was employed on 218 datasets with six design parameters as input variables and PSS as output. Moreover, the feature importance and the sensitivity analysis were performed to analyze quantitatively the feature importance and effect of the design parameters on the PSS. The results showed that, compared with the RMSE = 347.349, MAE = 210.019, and R 2 = 0.916 of the original SVR model, the PSO-SVR model reached better prediction performance with RMSE = 187.958, MAE = 135.889, and R 2 = 0.942. Among the six key design parameters, the effective depth of the slab D and the slab thickness H are the two main important factors that can cause large dispersion of the PSS in a stochastic environment and should be given more attention in the design and construction.