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Record W4293762779 · doi:10.24928/2022/0112

Assembly Process in Off-Site Construction: Self-Lock Device as a Key to a Lean Approach

2022· article· en· W4293762779 on OpenAlexaff
L. Picard, P. Blanchet, A. Bégin-Drolet

Bibliographic record

VenueAnnual Conference of the International Group for Lean Construction · 2022
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaUniversité Laval
Fundersnot available
KeywordsModular designTimelineLock (firearm)Process (computing)Key (lock)Computer sciencePlug and playEngineeringSystems engineeringManufacturing engineeringMechanical engineeringOperating system

Abstract

fetched live from OpenAlex

The implementation of lean construction in off-site construction is an ongoing combination aiming to improve the efficiency and reduce all forms of waste in the construction industry.Modular construction offers a high level of off-site value creation, and consequently leaner processes associated to the well-known off-site construction advantages as waste management, shorter project timeline, improved health and safety conditions for workers, better quality control, optimal material handling, and efficient working stations.Nonetheless, the on-site activities needed to connect the modules are often identified as critical sources of waste.In response, many connecting devices and models for calculations were developed in recent years, but very few present an automated locking mechanism for modular connection.While most connecting devices include the use of fasteners that need to be manually fixed to complete the connection of modules, an automated connecting device could significantly reduce the quantity of onsite activities by including an engineered mechanism that ensures self-lock.This research aims to evaluate the impact on leanness of an automated connecting device as well as to present a new plug-in self-lock device.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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 designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

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