Modeling and Sensitivity Analysis of Concrete Creep with Machine Learning Methods

Abstract

Although machine learning algorithms to predict the mechanical properties of concrete have been studied extensively, most of the research focused on the prediction of the strength of concrete and only a few studies have focused on concrete creep. This paper analyzed the maximum information correlation (MIC) between concrete creep influence parameters based on the updated Infrastructure Technology Institute of Northwestern University (NU-ITI) database, and the parameters in the database were adopted in the classical creep prediction models for calculation. Three machine learning algorithms (MLAs)—back-propagation artificial neural network (BPANN), support vector regression (SVR), and extreme learning machine (ELM)—were trained with the NU-ITI database to model concrete creep. The SVR-based model achieved high predictive accuracy. Sensitivity analysis of the parameters and feature selection of concrete creep were carried out based on the SVR and the Sobol method. By retraining the SVR model after feature selection, it was demonstrated that low-sensitivity and strongly correlated parameters will increase the robustness of machine learning models.