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Record W4312125615 · doi:10.3390/buildings12122185

Case-Study: Fully Prefabricated Wood Wall Connection to Improve Building Envelope and On-Site Efficiency

2022· article· en· W4312125615 on OpenAlexafffund
Etienne Julien, Pierre Blanchet, Louis Gosselin

Bibliographic record

VenueBuildings · 2022
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaMinistère des Forêts, de la Faune et des Parcs
KeywordsModular designJoint (building)EngineeringBuilding envelopeDoorsPrefabricationElevatorSeal (emblem)Expansion jointStructural engineeringEnvelope (radar)Bridge (graph theory)Computer scienceThermal

Abstract

fetched live from OpenAlex

As fully prefabricated wood walls (FPWW) are envisioned to increase building envelope performance, the junction between panels becomes crucial. Since FPWW restricts access to the inter-panel joints, it is preferable to generate an upstream mechanism to complete the joint automatically on-site. This study aimed to design a self-sealing joint for FPWW that would achieve high energy standards and accelerate on-site construction. Airtightness tests and thermal bridge assessments were conducted in the laboratory to compare the developed self-sealing joints with different sealing materials. These same tests were conducted on-site, in addition to observations of the assembly speed of conventional prefabricated walls and FPWW. Of all the materials tested, butyl tape showed the tightest connections. This material helps the joint developed to automatically seal adjacent walls spaced up to 7 mm apart. FPWW maximize the industrialization of conventional prefabricated walls by realizing the sealing details and the installation of doors, windows and exterior siding offsite. This way, FPWW could reduce the duration of a conventional single-family residential project. FPWW maximize quality control while reducing transportation costs associated with conventional modular solutions.

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 score0.348
Threshold uncertainty score0.685

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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