Prediction of concrete coefficient of thermal expansion and other properties using machine learning

Abstract

The coefficient of thermal expansion (CTE) significantly influences the performance of concrete. However, CTE measurements are both time consuming and expensive; therefore, CTE is often predicted from empirical equations based on historical data and concrete composition. In this work we demonstrate the application of linear regression and random forest machine learning methods to predict CTE and other properties from a database of Wisconsin concrete mixes. The random forest model accuracy, as assessed by cross-validation, is found to be significantly better than the American Association of State Highway and Transportation Officials (AASHTO) recommended prediction methods for CTE, denoted as level-2 and level-3.