Concrete compressive strength (CCS) is a critical parameter directly affecting the load-bearing capacity, durability, and overall safety of engineering structures. Traditional experimental approaches for determining CCS are time-consuming and costly, making predictive models an attractive alternative. In this study, thirteen different machine learning algorithms were applied to a well-established dataset (1030 samples, 8 input parameters) to estimate concrete compressive strength. Unlike many previous studies using the Yeh dataset that primarily emphasize prediction accuracy of individual models, this work presents a systematic multi-model comparison within a unified hyperparameter optimization framework. In addition to conventional performance metrics, permutation importance and SHAP-based explainability analyses are jointly employed, and detailed error evaluations are conducted across curing age and water-to-binder ratio subgroups to enhance engineering interpretability. Among the models tested, the CatBoost algorithm demonstrated the highest predictive performance (R² = 0.9469, RMSE = 3.70), followed closely by XGBoost, Gradient Boosting, and a stacking ensemble model. The results highlight that boosting-based machine learning models not only achieve high accuracy but also provide interpretable and robust predictions when evaluated through comprehensive error and explainability analyses.
Gürfidan et al. (Tue,) studied this question.