Towards Circular Construction: End-of-Life Scenario Analysis of Steel Structures Using Sustainable Life Cycle Approach
Bibliographic record
Abstract
The construction sector is responsible for 40% of the global natural resources exploitation, and 40% of CO2 emissions.Steel is a key construction material with significant potential to support the transition to a low carbon-built environment due to its durability, recyclability, and ease of reuse.Recycling steel components is the most widely practiced method for managing steel at the End-of-Life (EOL) phase of structures.However, there is a significant gap in the literature regarding the impact of reusing steel components in comparison to recycling them, considering simultaneously the environmental and economic impacts.This study introduces scenario analysis for the EOL phase of steel structures considering their Global Warming Potential (GWP), Energy Demand (ED), and the associated costs.The study has the following steps: (1) Reviewing studies of the EOL phase, focusing on Life Cycle Assessment (LCA) approaches and deconstruction optimization; (2) Defining the possible EOL scenarios of steel structures and developing the mathematical formulation; and (3) Performing environmental and economic analysis for these scenarios.The case of the Original Champlain Bridge deconstruction in Montreal, Canada, was selected to assess the EOL scenarios.The results of the analysis indicate that when reuse is maximized to 80%, GWP and ED decrease by nearly 65% and 76%, respectively, compared to full-recycling scenario, while costs decrease by 20%.Achieving 90-100% reuse is the most sustainable option but requires optimized logistics, efficient storage, and rigorous steel quality control to be viable.While recycling is necessary for components that cannot be reused, it should be minimized whenever possible due to its high energy and cost implications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".