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Record W4317851344 · doi:10.32920/21948623

Designing Buildings Using Reclaimed Steel Components

2023· preprint· en· W4317851344 on OpenAlexafffundabout
Mark Gorgolewski, Vera Straka, J. Edmonds, C. Sergio-Dzoutzidis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsToronto Metropolitan University
FundersNatural Resources CanadaUniversity of TorontoCanadian Institute of Steel Construction
KeywordsReuseComponent (thermodynamics)Work (physics)Architectural engineeringEngineeringConstruction engineeringCivil engineeringWaste managementMechanical engineering

Abstract

fetched live from OpenAlex

<p>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.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.266
Teacher spread0.184 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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
Published2023
Admission routes3
Has abstractyes

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