Research Status and Prospect of Machine Learning in Material Performance Prediction: A Case Study of Conventional and Solid Waste Concrete

Accelerated global urbanization places multiple pressures on the cement concrete industry, including resource consumption, carbon emissions, and solid waste disposal. Converting solid waste into construction materials offers a viable path towards its large-scale, high-value utilization, driving the green, low-carbon transition of the construction sector. However, solid waste concrete exhibits high-dimensional, multivariable, and strongly nonlinear characteristics owing to coupled factors such as waste type, source, substitution rate, mix proportions, and curing conditions. Consequently, traditional empirical methods fail to meet engineering needs, particularly in efficient mix design, performance prediction, and quality control. In conventional concrete research, predictive modeling has evolved from classical algorithms to ensemble and deep learning frameworks, often optimized via Bayesian optimization or genetic algorithms. Post-hoc interpretability methods such as SHAP and PDP are widely applied to accurately predict compressive strength, identify critical factors, reveal micro-mechanisms, and reconstruct empirical equations. In contrast, research on solid waste concrete has focused heavily on data challenges stemming from varied waste sources, including data heterogeneity, small sample sizes, and noise. In this field, swarm intelligence algorithms (such as particle swarm, grey wolf, and firefly algorithms) are commonly combined with ensemble models to improve prediction accuracy and engineering usability. However, limitations remain in data availability, feature consistency, cross-material transferability, and practical engineering application. Future research should leverage transfer learning, physics-informed neural networks (PINNs), and in-situ monitoring to advance models from standalone prediction tools into transferable, interpretable decision-support systems.