Artificial Intelligence-Enabled Material Passports for Construction Waste Traceability: A Systematic Review

Introduction
Construction and demolition waste (CDW) represents approximately 30 - 40% of global solid waste, with a low recovery rate for high-value materials. A key barrier is the lack of dynamic, lifecycle-spanning material information systems. Digital Material Passports (DMPs) integrated with Artificial Intelligence (AI) and Building Information Modelling (BIM) offer a theoretically compelling solution; however, existing frameworks are static, fragmented, and insufficiently validated. Therefore, the aim of this study is to systematically map current knowledge on AI-integrated DMP systems for CDW traceability, identify current limitations, and build the evidence base for a multi-layer circular recovery framework.
Methods
A systematic literature review, which followed PRISMA guidelines, was conducted by reviewing relevant papers published between 2015 and 2026 in the Scopus and Web of Science databases. The search strings combined terms relating to DMPs, BIM-CDW integration, AI/machine learning waste management, Internet of Things (IoT) material tracking, and circular construction. The inclusion criteria require peer-reviewed, English-language studies on digital technologies in CDW management or material lifecycle traceability. Thematic synthesis and bibliometric analysis were conducted using VOSviewer software to identify prominent research clusters, methodological trends, and evidence gaps.
Results
The findings reveal that BIM and AI applications in CDW management are mostly focused on design and construction phases, with minimal integration across demolition and recovery stages. In addition, this study found that DMP frameworks lack interoperability and real-time updating capability. Blockchain-based traceability and IoT integration remain nascent, with limited empirical evidence. Most significantly, there is a critical gap in studies that comprehensively integrate these technologies within a unified, life cycle assessment-supported operational pipeline.
Conclusions
In conclusion, this study provides evidence to support an AI-integrated DMP framework for high-value CDW circular recovery. The findings will inform framework development and have direct implications for digital construction policy, digital product passport regulation, and extended producer responsibility scheme design.