Designing Buildings Using Reclaimed Steel Components
Bibliographic record
Abstract
The consumption of non-renewable resources and the creation of wastes have been identifi ed as among the key issues that our society must address in order not to prejudice the opportunities of future generations. Yet the way we design and construct our buildings leads to huge volumes of waste being generated as well as the use of large amounts of materials, the extraction of which leads to considerable environmental damage. So, how can we design buildings in a way that creates closed loop materials systems that minimize waste generation and primary resourse use? The objective of this paper is to review work carried out at Ryerson University in Canada funded by NRCan and CISC to identify ways in which construction can set up reuse loops for steel components so that waste and the demand for primary steel are reduced. In particular, the design and construction issues related to the use of salvaged steel components will be reviewed, through a series of case studies to draw out lessons and conclusions about the implications of component reuse in construction. The case studies are of projects that reuse steel components from old buildings into new buildings. They suggest that opportunities for steel reuse are signifi cant but the industry needs to establish appropriate structures and cyclical systems and methods to ensure that components can be easily reclaimed from old buildings for reuse. Furthermore, certain ingrained industry design processes need to be overcome for reuse of steel (and other components) to become more acceptable.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".