Bridging the Gap in Recycled Aggregate Concrete (RAC) Prediction: State-of-the-Art Data-Driven Framework, Model Benchmarking, and Future AI Integration

Data-driven research on recycled aggregate concrete (RAC) has long faced the challenge of lacking a unified testing standard dataset, hindering accurate model evaluation and trust in predictive outcomes. This paper reviews critical parameters influencing mechanical properties in 35 RAC studies, compiles four datasets encompassing these parameters, and compiles the performance and key findings of 77 published data-driven models. Baseline capability tests are conducted on the nine most used models. The paper also outlines advanced methodological frameworks for future RAC research, examining the principles and challenges of physics-informed neural networks (PINNs) and generative adversarial networks (GANs), and employs SHAP and PDP tools to interpret model behaviour and enhance transparency. Findings indicate a clear trend toward integrated systems, hybrid models, and advanced optimization strategies, with integrated tree-based models showing superior performance across various prediction tasks. Based on this comprehensive review, we offer a recommendation for future research on how AI can be effectively oriented in RAC studies to support practical deployment and build confidence in data-driven approaches.