High-performance self-compacting concrete with recycled coarse aggregate : Soft-computing analysis of compressive strength

The growth of cities and industrialization has led to an increase in demand for concrete, resulting in resource depletion and environmental issues. Sustainable alternatives such as using recycled concrete aggregate (RCA) and industrial waste have been proposed to meet construction material demands while adhering to building codes and promoting sustainability. However, compressive strength (CS) is a crucial property of concrete, and the design parameters have different effects on CS for various grades. Recently, researchers have focused on partially replacing natural coarse aggregate (NCA) with RCA in concrete to achieve sustainability goals. This study aims to examine the influence of design parameters (w/c: water-cement ratio, w/b: water-binder ratio, A/c: total aggregate-cement ratio, FA/CA: fine-coarse aggregate ratio, SP: superplasticizer, w/s: water-solid ratio and RCA%) on concrete CS and address controversies in the insights gained from pairwise comparisons using Pearson's correlation coefficient (PCC) analysis. Additionally, five techniques (M5P, RF, SVM, LR, and ANNs) were used to predict the CS of high-performance self-compacting concrete (HP-SCC) with RCA, and the results were compared with an ANNs-based model as was the commonly used one in literature. The approaches were assessed based on their accuracy measured using correlation coefficient (CC), mean absolute error (MAE), Root Mean Square Error (RMSE), Mean absolute percentage error (MAPE), Scatter index (SI), and comprehensive measure (COM) indicators. Accordingly, the analysis indicated that SVM-PUK-based model is the most appropriate and effective technique to predict the CS of HP-SCC for the given datasets, with CC = 0.894, 0.900, MAE = 1.721, 3.813, RMSE = 5.137, 6.306, and MAPE = 4.5%, 7.6% for the training and testing stages, respectively. The uncertainty analysis results were 21%, 20.7%, 19%, 22%, and 19% for M5P, RF, SVM, LR, and ANN-based models, respectively, whereby all of them were under
QC 20230901