<![CDATA[Ultra‑High‑Performance Concrete (UHPC) is a considerably advanced cementitious concrete with great characteristics of strength and durability, but the compressive strength is highly dependent on the multi‑faceted interplay between mixture proportions and curing conditions. These interactions are nonlinear and multivariate, making it difficult to accurately estimate the UHPC compressive strength using the previous experimental and empirical methods. In the paper, a Random Forest (RF) regression model has been constructed to estimate UHPC compressive strength based on a large‑scale dataset of 810 samples and 13 predictors (material composition and curing parameters). Multiple statistical measures were strictly used to evaluate the performance of the model, such as R2, RMSE, MAE, MAPE, and CVRMSE, as well as 10‑fold cross‑validation to evaluate stability and ability to generalize. The optimized RF model had a high predictive accuracy with a value of 0.96 on the testing set and small values of errors, which showed high robustness and consistency in diverse segmentations of data. Hyperparameter tuning also improved the model performance by finding a balance between model complexity and generalization. SHAP (Shapley Additive Explanations) analysis was used to enhance the transparency and interpretability of the models, to measure the contribution of the individual input feature to the compressive strength predictions. The findings demonstrated that curing age, fibre, silica fume, and dosage of superplasticizer were the most significant parameters that controlled the strength development of UHPC. The suggested modeling framework reveals the efficiency of bringing ensemble machine learning along with explainable artificial intelligence methods to provide accurate, reliable, and interpretable predictions of UHPC compressive strength, which creates a useful instrument in the process of mix design optimization and performance evaluation.]]>
Copyrights © 2025
