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Record W4296168358 · doi:10.29173/mocs259

Modular construction and circularity. A case study of a mass timber design studio

2022· article· en· W4296168358 on OpenAlexvenueno aff
Rafael Novais Passarelli

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsModular designStudioArchitectural engineeringEngineeringDesign studioConstruction engineeringComputer science

Abstract

fetched live from OpenAlex

The increased use of wood-based materials such as CLT (Cross Laminated Timber) can reduce the GHG emissions of the construction sector. Likewise, offsite and modular construction methods can lead to more efficient material use, reducing construction-generated solid waste. However, it is worth noting that employing mass timber and modular construction is not automatically beneficial under all circumstances. The transition from the current linear, high-impact, and wasteful construction practices to a circular, regenerative one can offer an alternative solution to the problem. Moreover, high education institutions can play an influential role in this transition. However, there is a knowledge gap regarding education for circularity in architectural design. This paper aims to address this gap. It presents an educational approach integrating circular design principles with mass timber and modular construction in the setting of an architectural design studio. This paper analyses the pedagogical methods employed and the learning outcomes of the design studio. The results showed students successfully integrated architectural design and knowledge of modular mass timber technology with an innovative circular rationale and exceeded the learning outcomes in two cases.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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