Enhancing Circularity in the Building Industry: A Review of Material Passports and Digital Twin Technologies
Bibliographic record
Abstract
Material passports (MPs) enhance the reuse and recycling of building materials by archiving detailed information, while digital twin (DT) technologies streamline data management via virtual representations and real-time updates across the supply chain. Integrating these tools presents significant opportunities to support circular strategies in the construction industry. This systematic review assesses the current state of research, emerging technologies, and key methodologies for DT and MP adoption. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols, academic and practice-based publications examining MP regulatory frameworks, technological advancements, and functionalities were systematically reviewed. The review identifies critical gaps in the integration of advanced technologies such as artificial intelligence (AI) and Internet of Things (IoT), highlights the limited scalability of current MP frameworks, and proposes a path forward through dynamic material passports (DMPs)—a concept where MPs are continuously updated throughout a building’s lifecycle via DT integration. The primary contribution of this study lies in synthesizing fragmented literature on MPs and DTs, proposing a comprehensive and lifecycle-oriented framework to enable real-time material tracking, and highlighting the transition toward scalable, data-driven circular solutions in construction. The review reveals the following findings: (1) inadequate investigation into AI and IoT technologies within the MP context; (2) the potential benefit of combining MPs with DT technology to create DMPs; and (3) limited applicability of current methodologies to existing building stocks, as they are often complex and tailored to new construction projects. These findings offer a foundation for future development and adoption of DMPs, and inform policies, practices, and digital strategies aimed at enhancing circularity in the built environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".