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Record W2902218282 · doi:10.22215/etd/2017-11923

Deconstruction and Design for Disassembly: Analyzing Building Material Salvage and Reuse

2017· dissertation· en· W2902218282 on OpenAlexaboutno aff
Taylor Balodis

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsReuseDemolitionDeconstruction (building)Architectural engineeringSituatedEngineeringConstruction engineeringCivil engineeringWorkflowComputer scienceWaste management

Abstract

fetched live from OpenAlex

Building construction and demolition waste greatly contribute to the total mass found in landfills. While the traditional linear building model demands new materials for new projects, adopting cyclical models of reclamation and reuse will provide a more resourceful and sustainable future.This thesis proposes designing a building for disassembly by reusing salvaged materials in the Vanier neighbourhood of Ottawa. As the last major area near downtown that has yet to be developed, Vanier is a model that represents places where aging structures provide an opportunity for material evolution among the existing urban fabric, and where land value is beginning to outweigh the value in rehabilitating obsolete buildings situated on it.Utilizing digital workflows, this thesis project will examine deconstructing derelict buildings and structures found on several Vanier properties as the base material palette for designing a new addition to an existing commercial structure with the ability to be methodically disassembled.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.255
Teacher spread0.242 · 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 designQualitative
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

Citations3
Published2017
Admission routes1
Has abstractyes

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