A Conceptual Framework for Enabling Structural Steel Reuse Utilizing Circular Economy in Modular Construction
Bibliographic record
Abstract
Steel production is a major contributor to resource use and greenhouse gas emissions, accounting for nearly 8% of global CO2 emissions, with structural steel accounting for more than half of this share. As the construction sector moves toward decarbonization, strategies for reuse, particularly within Modular and Offsite Construction (MOC), are receiving growing attention. This study presents a digitally integrated framework for the recovery, testing, and reuse of structural steel in MOC, grounded in Circular Economy (CE) principles. The framework is based on a systematic review of 162 academic, industry, and case study records and is structured across four stages: (1) material recovery, (2) testing and certification, (3) integration into MOC, and (4) performance evaluation. Building Information Modeling (BIM) and Material Passports (MPs) provide digital infrastructure for traceability, documentation, and compliance with established protocols, such as SCI P427 and P440. Reuse outcomes are assessed using the Modular Reuse Ratio (MRR), Carbon Savings (CS), Lifecycle Cost Savings (LCS), and a tailored Material Circularity Indicator (MCI). By aligning certification requirements with digital processes, the framework addresses current gaps in traceability, standardization, and decision support. It provides a scalable and replicable model that advances structural steel reuse, contributes to sector-wide decarbonization, and supports alignment with emerging CE and performance-based certification schemes.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".