Yanxia Liu

Machine Learning for Concrete Performance Prediction and Intelligent Optimization: A Comprehensive Review

Comparison analysis indicates that ensemble tree-based models—particularly random forest and gradient-boosting variants such as XGBoost—together with well-tuned neural networks consistently achieve the highest predictive accuracy across most concrete properties, whereas limited data availability, inconsistent validation protocols, and restricted model interpretability remain the principal obstacles to engineering deployment.