Application Research Progress of Solid Waste Concrete Based on Machine Learning Technology

With accelerating urbanization, China generates large volumes of construction waste annually, much of which is landfilled, causing environmental damage and wasting recyclable resources. Traditional recycling technologies struggle with complex material compositions and performance degradation, limiting large-scale reuse. This highlights the need for intelligent, data-driven solutions to enhance solid waste recycling efficiency. Recent developments in machine learning (ML) have enabled more accurate performance prediction and optimization of waste concrete, offering new pathways for material regeneration. ML’s nonlinear modeling and pattern recognition capabilities are particularly suited to capturing the complex behaviors of recycled materials. However, systematic reviews on ML applications across various waste concretes are still lacking, and future research directions remain unclear. This study conducts a scientometric analysis of 1762 publications (2011–2024) from the Web of Science Core Collection (WOSCC) and CNKI, aiming to map current trends and guide future research on ML applications in waste concrete recycling. The findings show a clear evolution: research has expanded from recycled concrete to a wider range of modified solid waste concretes. ML applications have advanced from compressive strength prediction to multi-objective optimization involving durability, cost, and mechanical performance. Algorithm development and model accuracy have improved steadily. Key challenges persist, including limited data quality and scale, which constrain complex model training. Research on specific properties such as salt-frost resistance is scarce due to high testing costs. Models often lack generalizability in coupled conditions (e.g., salt-frost–carbonation) and suffer from poor physical interpretability, hindering understanding of durability mechanisms. Nevertheless, unresolved issues remain, including the scarcity of standardized datasets, the limited integration of domain knowledge with ML methods, and the lack of comprehensive evaluations across multiple degradation mechanisms, all of which represent critical research gaps for future studies.