Towards Circular Construction: Material and Component Stock Assessment in Montréal’s Residential Buildings
Bibliographic record
Abstract
The construction industry is a major consumer of raw materials and a significant contributor to global waste. In Canada, the construction, renovation, and demolition (CRD) sector diverts only 16% of its waste from landfills, underscoring the urgent need for circular economy (CE) practices. This study develops a generalizable and reproducible framework for archetype identification to support CE strategies, with a focus on Montréal, Canada’s second-largest city. We define a new set of exterior shell archetypes for low-rise residential buildings and demonstrate their application in a neighborhood-scale case study. These archetypes enable systematic estimation of material and component stocks, as well as end-of-life recovery flows, across a representative sample of buildings in the Mercier–Hochelaga–Maisonneuve district. Results show that prioritizing reuse can nearly double material recovery compared to conventional sorting and recycling. More broadly, this framework advances engineering design for circular systems by integrating component-level data into reuse strategy assessment and providing a scalable approach for urban circularity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".