Concrete Compressive Strength Prediction Using Machine Learning: A State-of-the-Art Review

Concrete compressive strength (CCS) is a critical parameter for structural performance, durability, and safety. Traditional strength assessment methods rely on destructive testing, which is time-consuming and cost-intensive. Over the past three decades, machine learning (ML) has emerged as a powerful alternative for predicting CCS using mix design parameters, curing conditions, and environmental factors. This review synthesizes findings from 25 key studies published between 1998 and 2025, tracing the evolution of ML-based CCS prediction from early artificial neural networks (ANNs) to advanced deep learning and hybrid meta-heuristic models. The paper examines dataset characteristics, key predictors, and the performance of classical algorithms (SVR, decision trees), ensemble methods (Random Forest, Gradient Boosting, XGBoost), deep neural networks, and optimization-enhanced frameworks. Interpretability approaches such as SHAP and feature importance analysis are highlighted as essential for engineering adoption. Comparative evaluations reveal that boosting algorithms and meta-heuristic-enhanced ANNs consistently outperform other models, while deep learning excels on large datasets. Despite significant progress, challenges remain in dataset standardization, model transparency, and integration with building codes. Future research directions include physics-informed ML, IoT-enabled real-time prediction, and explainable frameworks aligned with structural standards. This review underscores ML's transformative potential in concrete engineering, paving the way for sustainable, efficient, and data-driven construction practices.