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Record W2937669126 · doi:10.29173/mocs11

3d/4d Visualization Framework for Modelling Off-Site Productivity of Modular Construction Housing

2016· article· en· W2937669126 on OpenAlexaffvenue
Jad Jureidini, Mona Afifi, Rita Zhang, Mohamed Al‐Hussein

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsModular designVisualizationProductivityComputer scienceProcess (computing)Modular constructionSystems engineeringConstruction engineeringSoftwareBuilding information modelingEngineeringSoftware engineeringEngineering managementOperations managementScheduling (production processes)

Abstract

fetched live from OpenAlex

Construction, as the process of constructing a building, has diverse range of available software tools which have been developed to support modelling operations, tasks, and processes in the construction industry. 3D visualization as a modelling tool has been widely used to improve modular and off-site construction activities; it allows for seamless information sharing and collaboration among project stakeholders, and also provides opportunities for improvements suggested by the project team. This paper presents a framework for modelling the off-site modular construction of housing at the Kent Homes manufacturing facility through 3D/4D visualization. A case study is represented to illustrate the potential for production improvement.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.213
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 designSimulation or modeling
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

Citations2
Published2016
Admission routes2
Has abstractyes

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