Prediction of Compressive Strength and Elastic Modulus for Recycled Aggregate Concrete Based on AutoGluon

Abstract

While the civil construction industry brings great convenience to life, the large amount of waste concrete also poses a significant problem of construction waste disposal. As one of the effective ways to utilize waste concrete, recycled aggregate concrete (RAC) can improve the environment while reducing the consumption of construction materials. This study aims to use AutoGluon (AG), an automated machine learning platform, to predict both the compressive strength and elastic modulus of RAC. Then the performance of AG is compared with traditional empirical formulas and multiple linear regression models. The determination coefficient (R2) is chosen as one of the evaluation standards for predicting values. The results demonstrate that the WeightedEnsemble model of AG performed best in predicting both the compressive strength and elastic modulus, which provides a new method for the rapid and accurate prediction of the properties of RAC in engineering construction.