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Record W2943191952 · doi:10.1139/cjce-2018-0411

A visual dashboard to monitor BIM model dynamics

2019· article· en· W2943191952 on OpenAlexaffvenue
Hisham Abou-Ibrahim, Farook Hamzeh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDashboardTimelineBuilding information modelingProcess (computing)Computer scienceTrack (disk drive)System dynamicsSystems engineeringProject managementVisibilityInformation modelProcess managementEngineeringSoftware engineeringOperations managementArtificial intelligence

Abstract

fetched live from OpenAlex

The use of Building Information Modeling (BIM) is on the rise in the construction industry as more of its potential is being harnessed. The evolution of BIM models is a dynamic process where a project’s information is progressively developing during a project’s timeline. Although several studies track a project’s evolution from product and process perspectives, they do not necessarily track actual model dynamics occurring at the level of model elements for design management purposes. In this regard, this study introduces five variables to track changes witnessed across BIM model development using a visual dashboard designed to facilitate their interpretation and use by practitioners. The study applies the developed dashboard on an empirical case study project to test its usefulness and shortcomings. Results show that the dashboard can convey several design management aspects and can help design managers better maneuver the development of their designs.

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)0.005
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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
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

Citations7
Published2019
Admission routes2
Has abstractyes

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