ANN‐based analysis of the effect of SCM on recycled aggregate concrete

Abstract Rising environmental awareness has prompted in‐depth studies on how the concrete industry affects the environment. Using recycled concrete aggregates (RCAs) and supplementary cementitious materials (SCMs) in concrete manufacturing provides advantages for sustainability. However, the broader chemical composition of SCMs and the inferior qualities of RCAs compared with natural aggregates (NAs) often lead to a decrease in concrete mechanical strength. The difficulty lies in foreseeing how the inclusion of SCMs and RCAs will affect the concrete compressive strength. The artificial neural network (ANN) approach presented herein can precisely forecast the recycled aggregate concrete (RAC) compressive strength, even when incorporates SCMs. The analysis employing the connection weight approach (CWA) determines how input variables influence compressive strength. Results indicate silica fume contributes most to compressive strength, followed by cement content, silica modulus, fine natural aggregate dosage, and coarse natural aggregate. Additionally, the amount of water utilized, the water/cement ratio, and the presence of RCA are all detrimental to compressive strength. The adverse effect of the cementitious materials' alumina modulus can be attributed to increased water demand during their reaction. Performance metrics of the final ANN model on the testing data subset include R 2 = 0.94, and RMSE = 3.11, utilizing 834 data observations after outlier treatment for training and validation purposes. In summary, the ANN‐based approach demonstrates its efficacy in predicting concrete compressive strength when incorporating SCMs and RCAs, shedding light on the influential factors in concrete performance.