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Record W4402192846 · doi:10.32920/26883484

Improving Circularity in Ontario Building Design Through Design for Disassembly Principles

2024· preprint· en· W4402192846 on OpenAlexaboutno aff
Stephanie Tzanis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectural engineeringDesign elements and principlesComputer scienceEngineeringSystems engineeringConstruction engineering

Abstract

fetched live from OpenAlex

Mainstream construction processes follow a linear model, which puts pressure on natural resources, produces excessive waste, and increases carbon emissions. Circular design approaches, such as design for disassembly (DfD), can mitigate the environmental damage already done. DfD considers a building's end of life from the design phase, allowing components to be easily dismantled and reused. This research investigates the level of awareness of design for disassembly in Ontario building design and construction, the barriers to improvement and adoption, and whether a circularity evaluation tool would be beneficial in addressing these barriers. An online survey of the industry was conducted, including responses from architects, engineers, researchers, and construction managers. Individual interviews were also conducted. Finally, the evaluation tool Regenerate was assessed using real building projects. The results of this study identify the industry's knowledge gaps and suggest methods to improve uptake of circular design strategies through resources, policy, and assessment tools.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.912

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.070
GPT teacher head0.260
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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