Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models

Abstract

Artificial intelligence methods like machine learning (M-L) are cutting-edge methods for evaluating the material attributes and impact of influencing parameters, thereby eliminating the unneeded tests in the laboratory. This study focused on developing estimation models for the compressive strength (C-S) of self-compacting concrete (SCC) using gene expression programming (GEP) and multi-gene expression programming (MEP) M-L methods. Prior M-L research on SCC lacks building mathematical equations for the strength estimation. Therefore, this study proposed mathematical equations for the build M-L models. In addition, a sensitivity analysis and interaction study were performed to investigate the influence and correlation of inputs. The proposed M-L models exhibited better precision performance, and the estimated results agreed well with the actual data based on the statistical measures. The comparison of both models showed that MEP was more accurate, with an R2 of 0.89, than the GEP, with an R2 of 0.85. The relative percentage impact of raw materials from the sensitivity analysis exhibited that the crucial ingredient was super-plasticizer, followed by silica fume, water, and cement. The interaction study indicated that increasing cement content had a favorable relationship, super-plasticizer, fly ash, and silica fume had, and water and coarse and fine aggregate had a negative effect.