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Record W4296168341 · doi:10.29173/mocs264

AWP for residential buildings to modular construction: A proposed framework

2022· article· en· W4296168341 on OpenAlexvenueno aff
Slim Rebai, Olfa Hamdi, Samer BuHamdan, Zoubeir Lafhaj, Pascal Yim

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2022
Typearticle
Languageen
FieldEngineering
TopicTransport and Logistics Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsConstructabilityModular designProductivityModularity (biology)Work (physics)EngineeringArchitectural engineeringRisk analysis (engineering)Systems engineeringComputer scienceBusiness

Abstract

fetched live from OpenAlex

The demand on affording homes for the growing population in the world is noticeably increasing. This demand creates a high pressure to find solutions that can help in constructing quick and affordable shelters. In efforts to improve productivity, performance, and constructability of residential buildings, the direction toward modularity in construction is increasing. In order to gain the best benefits of modular systems in construction, these systems should be associated with project management practices. Among these practices, Advanced Work Packaging (AWP) is a construction driven planning tool that proved its value in improve the delivery and execution effectiveness in the oil and gas fields and currently is witnessing greater consideration in the construction industry. The current study aims to link the two concepts through proposing a framework to integrate AWP in residential modular construction. This article is also presenting an example about the possible way for this integration. The article also discusses the opportunity to improve productivity and installation costs due to the implementation of the proposed framework.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations5
Published2022
Admission routes1
Has abstractyes

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