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A comprehensive survey of data-driven technologies for construction solid waste recycling systems
Fangyu Li1, Mohan Niu1, Honggui Han1
1School of Information Science and Technology, Beijing University of Technology, Beijing, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing University of Technology, Beijing, China.
None:
Construction solid waste (CSW) recycling is a growing challenge for smart construction. The challenge mainly arises from the heterogeneous composition of waste streams, fragmented recycling workflows, and limited capabilities for real-time sensing, adaptive control, and coordinated digital management across recycling stages. The improvement of CSW recycling systems plays a crucial role in achieving green cities and societies by reducing environmental burdens and supporting low-carbon urban regeneration. Recent developments in data-driven technologies provide new opportunities to enhance CSW recycling systems. This survey reviews these technologies and their roles across the recycling pipeline. Multi-source data and learning methods support early-stage material recognition; adaptive strategies improve crushing, screening, and sorting; and digital platforms enable inventory tracking, emission monitoring, and energy regulation. Key challenges remain, including the heterogeneous nature of CSW and potential instability under real-time conditions. The balance among energy consumption, processing speed, and system robustness also poses practical constraints. By connecting insights from CSW processing and computational intelligence, this survey establishes a structured knowledge base and outlines future research directions toward efficient CSW recycling systems.
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