The implications of reuse and recycling for the design of steel buildings
Bibliographic record
Abstract
There is an increasing interest in reuse and recycling in the Canadian construction industry. This interest is driven partly by the recent adoption in Canada of the Leadership in Energy and Environmental Design (LEED®) Green Building Rating System and partly by a greater general awareness of environmental issues. Designers are beginning to look at how to incorporate reused steel components into construction projects, thereby reducing greenhouse gas emissions by saving on primary steel production. However, although some designers are willing to redesign their projects to make use of available reclaimed structural steel components, it is often difficult to identify suitable materials in the local area at the appropriate time in the life of a project. A limiting factor is that designers, construction companies, and others perceive a lack of a well-established and easily available mechanism for exchange of reclaimed components. This paper reviews the issues that are relevant to increasing recycling and reuse in construction and focuses on examples that illustrate the benefits that steel can bring to sustainable construction. In particular, it discusses the issues relevant to designing to enable future disassembly and the way in which steel components can be readily reused.Key words: reclaimed steel, reuse of materials, steel recycling, design for deconstruction, sustainable construction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".