Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate

Abstract Self-compacting concrete (SCC) is a type of known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) self-compacting containing recycled plastic aggregates (RPA). A database 400 experimental data sets was assess capabilities multi-objective genetic algorithm evolutionary polynomial regression (MOGA-EPR) gene expression programming (GEP). The analysis results indicated that proposed equations provided more accurate CS predictions than traditional such as linear model (LRM). achieved lower mean absolute error (MAE) root square (RMSE) values, close optimum value (1.0), higher coefficient determination ( R 2 ) LRM. As such, can be utilized obtain reliable design calculations better in SCC incorporating RPA.