Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixtures

Abstract

The concrete compressive strength is essential for the design and durability of concrete infrastructure. Silica fume (SF), as a cementitious material, has been shown to improve the durability and mechanical properties of concrete. This study aims to predict the compressive strength of concrete containing SF by dual‐objective optimization to determine the best balance between accurate prediction and model simplicity. A comprehensive dataset of 2995 concrete samples containing SF was collected from 36 peer‐reviewed studies ranging from 5% to 30% by cement weight. Input variables included curing time, SF content, water‐to‐cement ratio, aggregates, superplasticizer levels, and slump characteristics in the modeling process. The gray wolf optimization (GWO) algorithm was applied to create a model that balances parsimony with an acceptable error threshold. A determination coefficient (R2) of 0.973 demonstrated that the CatBoost algorithm emerged as a superior predictive tool within the boosting ensemble context. A sensitivity analysis confirmed the robustness of the model, identifying curing time as the predominant influence on the compressive strength of SF‐containing concrete. To further enhance the applicability of this research, the authors proposed a web application that facilitates users to estimate the compressive strength using the optimized CatBoost algorithm by following the link: https://sf-concrete-cs-prediction-by-javid-toufigh.streamlit.app/.