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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.
Waste Management (New York, N.Y.)
|June 23, 2026
Summary
Recycling construction solid waste (CSW) is complex due to its varied nature and fragmented processes. Data-driven technologies offer solutions for smarter CSW recycling, improving efficiency and sustainability in urban regeneration.
Area of Science:
- Environmental Science
- Civil Engineering
- Computer Science
Background:
- Construction solid waste (CSW) recycling faces challenges from heterogeneous waste, fragmented workflows, and limited digital management.
- Improving CSW recycling is vital for green cities, reducing environmental impact, and supporting low-carbon urban regeneration.
Purpose of the Study:
- To review data-driven technologies for enhancing construction solid waste (CSW) recycling systems.
- To explore the application of these technologies across the CSW recycling pipeline.
- To identify challenges and future research directions for efficient CSW recycling.
Main Methods:
- Survey of recent developments in data-driven technologies relevant to CSW recycling.
- Analysis of how multi-source data, learning methods, adaptive strategies, and digital platforms are applied.
- Connecting insights from CSW processing and computational intelligence.
Main Results:
- Data-driven technologies enable early-stage material recognition using multi-source data and learning methods.
- Adaptive strategies enhance crushing, screening, and sorting processes.
- Digital platforms facilitate inventory tracking, emission monitoring, and energy regulation.
Conclusions:
- Data-driven technologies present significant opportunities to improve CSW recycling efficiency and sustainability.
- Challenges include CSW heterogeneity, real-time instability, and balancing energy consumption, processing speed, and robustness.
- Future research should focus on overcoming these challenges for more efficient CSW recycling systems.
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