Development of hybrid gradient boosting models for predicting the compressive strength of high-volume fly ash self-compacting concrete with silica fume

Abstract

In an effort to extend the service life of structures under harsh exposure conditions and reduce the carbon dioxide emissions from cement production, researchers are examining the performance of cement concretes with different mineral admixtures. This study focuses on self-compacting concrete (SCC) incorporating high-volume fly ash (HVFA) and silica fume (SF) as supplementary cementitious materials (SCMs). The aim is to investigate the fresh and hardened properties, particularly the compressive strength, of these combinations. SCMs were used to replace varying amounts of cement, with SF showing the greatest improvement in mechanical characteristics across all age groups. Advanced modelling techniques, including Gradient Boosting (GB), Gradient Boosting-Particle Swarm Optimization, Gradient Boosting-Bayesian Optimization, and Gradient Boosting-Differential Evolution (GB-DE), were employed to analyse the strength characteristics of HVFA-SCC. The GB-DE model emerged as the most accurate, with an R² value of 0.995029 and an RMSE of 0.023862. Additionally, an open-source GUI based on GB-DE is introduced to provide engineers with a transparent tool for optimizing concrete mix designs, addressing the opacity of traditional machine learning models.