Development of a novel compressive strength design equation for natural and recycled aggregate concrete through advanced computational modeling

Owing to the variations in the recycled coarse aggregates (RCA) characteristics, the compressive strength prediction of recycled aggregate concrete (RAC) is a complex challenge causing hindrance in the design guidelines development and practical application of RAC. This study aims to develop a unified compressive strength model for the RAC and natural aggregate concrete (NAC) independent of RCA source and other properties. For this reason, four input parameters, including water absorption of coarse aggregates, effective water-to-cement ratio, coarse aggregates to cement ratio, and RCA replacement ratio, are considered to predict the compressive strength of NAC and RAC. Ten machine-learning techniques, including random forest, gradient boost, Ada boost, k-nearest neighbor, bagging regressor, support vector, XG boost, decision tree, artificial neural network, and gene expression programming, are evaluated through a test database having 962 experimental results of compressive strength of NAC and RAC from 107 different studies. The performance of machine-learning algorithms is assessed through various statistical parameters. Results show that the input parameters (considered in this study) are essential in predicting the cubic and cylindrical compressive strength of NAC and RAC. The machine learning models and comprehensive design equations developed in this study are better than existing models and can be recommended as an effective tool for predicting the compressive strength of NAC and RAC having RCA from different sources, leading toward the development of sustainable concrete design guidelines.