Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques

Splitting tensile strength is one of the commonly used mechanical properties in the design of high-performance concrete (HPC) structures. To achieve accurate prediction of splitting tensile strength of HPC, two optimized machine learning models GA-ANN and GS-SVR were employed to predict the target tensile strength on 714 sets of sample data with 12 features of HPC as input variables. The appropriate initial weights and thresholds of the ANN model and hyper-parameters of the SVR model were first obtained by genetic algorithm and grid search, respectively. Then, the optimized models were used to train and test the dataset, and the performance of the models was evaluated and compared using evaluation metrics. The results showed that the prediction accuracy of the optimized GA-ANN model was higher than that of the pre-optimized ANN model, but still inferior to that of the GS-SVR model. Compared with the known machine learning models in the literature, the GS-SVR model proposed in this paper has smaller prediction error, higher prediction accuracy, and better performance. The strong generalization ability of the GS-SVR model to unseen test data indicates its great potential in predicting the tensile strength and is recommended as an alternative method for splitting tensile strength prediction of HPC. Additionally, a parametric analysis based on the Shapley additive explanations approach (SHAP) was proposed to investigate the importance and contribution of these input variables on the output splitting tensile strength.