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Record W4406703736 · doi:10.3390/buildings15030321

Challenges in the Design for Disassembly of Light Timber Framing Panelized Components

2025· article· en· W4406703736 on OpenAlexafffund
Víctor Hugo Torres, Guillermo Íñiguez-González, Pierre Blanchet, Baptiste Giorgio

Bibliographic record

VenueBuildings · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversité LavalNatural Sciences and Engineering Research Council of Canada
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversité LavalJohn D. and Catherine T. MacArthur Foundation
KeywordsFraming (construction)Architectural engineeringEngineeringComputer scienceStructural engineering

Abstract

fetched live from OpenAlex

The construction sector generates more than one-third of global waste. Although there is a consensus on the need to reduce it, empirical research evaluating current systems to develop circular solutions remains limited. Using a full-scale model, this article evaluates the disassemblability of the corner joint between two prefabricated lightweight timber-framed walls, a system widely adopted in residential construction in North America. The analysis deconstructed the disassembly actions, identified their level of difficulty, and classified the recovered materials into three categories: reusable, recyclable, and waste. The results reveal that the lack of design criteria for disassembly significantly limits the system’s circularity, as it prioritizes assembly speed and energy performance. The predominant use of nails as fasteners complicates the separation of layers, damages materials, and restricts their reuse. This highlights the urgent need to redesign construction solutions that enable efficient disassembly, promote component recovery, and extend their time in circulation. This study establishes a foundation for the evolution of lightweight timber-framed panel design toward systems more aligned with circularity principles.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.269
Teacher spread0.209 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations10
Published2025
Admission routes2
Has abstractyes

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