Prediction of Compressive Strength of Concrete Using Explainable Machine Learning Models
H. Fu; Xiong Zhou; Pengfei Xu; Dandan Sun · 2025 · Materials
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Predicting the compressive strength of concrete is essential for engineering design and quality assurance. Traditional empirical formulas often fall short in capturing complex multi-factor interactions and nonlinear relationships. This study employs an interpretable machine learning framework using Gradient Boosting Trees, Random Forest, and Backpropagation Neural Networks to predict concrete compressive strength. Bayesian optimization was employed for hyperparameter tuning, and SHAP analysis was used to quantify feature contributions. Based on 223 sets of compression test data, this study sys
Abstract by H. Fu; Xiong Zhou; Pengfei Xu; Dandan Sun, Materials (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/ma18215009