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

Javid, A. R ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1002/suco.202301135
  3. Publisher: 2024
  4. Abstract:
  5. 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/. © 2024 fib. International Federation for Structural Concrete
  6. Keywords:
  7. Concrete ; Cements ; Concrete mixtures ; Curing ; Durability ; Forecasting ; Machine learning ; Sensitivity analysis ; Silica fume ; Boosting ensemble machine learning ; Boosting ensembles ; Cementitious materials ; Concrete compressive strength ; Curing time ; Durability of concretes ; Gray wolf optimization ; Gray wolves ; Optimisations ; Compressive strength
  8. Source: Structural Concrete ; Volume 25, Issue 5 , 2024 , Pages 4048-4074 ; 14644177 (ISSN)
  9. URL: https://onlinelibrary.wiley.com/doi/10.1002/suco.202301135