Smart prediction: Hybrid random forest for high-volume fly ash self-compacting concrete strength

Abstract

Sustainable development in the concrete industry necessitates a standardized framework for material development, despite promising experimental results. High-volume fly ash (HVFA) self-compacting concrete’s (SCC) strength characteristics are investigated in this study through the use of sophisticated modeling techniques such as random forest (RF), RF-particle swarm optimization, RF-Bayesian optimization, and RF-differential evolution (RF-DE). Cement was partially replaced with HVFA and silica fume (SF), enhancing fresh and hardened concrete properties such as compressive and split-tensile strengths, passing ability, and filler capacity. Input parameters included cement, SF, fly ash, T-500-time, maximum spread diameter, L-box blocking ratio, J-ring test, V-funnel time, and age. Statistical tools like uncertainty analysis, SHapley Additive exPlanations, and regression error characteristic curves validated the models. The RF-DE model showed the best predictive accuracy among them. Machine learning (ML) is great at predicting compressive strength (CS), but SCC-mix engineers have a hard time understanding it because of its “black-box” nature. To address this, an open-source graphical user interface based on RF-DE was developed, offering precise CS predictions for diverse mix conditions. This user-friendly tool empowers engineers to optimize mix proportions, supporting sustainable concrete design and facilitating the practical application of ML in the industry.

Graphical abstract

Keywords

high-volume fly ash / silica fume / self-compacting concrete / random forest / differential evolution / Bayesian optimization / particle swarm optimization

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