Flexural performance tests and machine learning analysis of prestressed high strength steel reinforced concrete beams

Abstract

ABSTRACT This paper reports a study on the flexural performance of prestressed high strength steel reinforced concrete (PHSSRC) beams and proposes machine learning (ML) models for predicting the flexural bearing capacity of steel reinforced concrete (SRC) beams. Seven prestressed steel reinforced concrete (PSRC) beams were tested under a four-point bending load to investigate the effect of steel strength grade, steel ratio, and longitudinal reinforcement ratio on the flexural load capacity of beams. The test results indicated that increasing the steel strength grade and steel ratio significantly enhanced the bearing capacity of PSRC beams. A database consisting of 112 sets of experimental data on the flexural performance of SRC beams, including the test data in this study, was established for training and validating the ML models. Artificial Neural Network (ANN) model and eXtreme Gradient Boosting (XGBoost) model were respectively developed to predict the flexural load capacity of SRC beams, and the accuracy of the flexural load capacity values predicted by the design specification, the ANN model and the XGBoost model were compared. The results show that the XGBoost model has the best prediction performance. Finally, the sensitivity analysis of the parameters was performed using the Shapley Additivity (SHAP) interpretation method.