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Record W2942550638 · doi:10.1139/cjce-2018-0287

BIM-based building design coordination: processes, bottlenecks, and considerations

2019· article· en· W2942550638 on OpenAlexaffvenue
Sarmad Mehrbod, Sheryl Staub‐French, Melanie Tory

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBuilding information modelingProcess (computing)DocumentationTerminologySystems engineeringUnavailabilityQuality (philosophy)Risk analysis (engineering)Engineering design processComputer scienceDesign processProcess managementEngineeringWork in processReliability engineeringOperations managementBusinessScheduling (production processes)

Abstract

fetched live from OpenAlex

Successful management of the building design coordination process is critical to the efficient delivery of cost-effective and quality projects. The traditional setting of design coordination, however, is inefficient and error-prone. Building information modelling (BIM) has proven valuable for increasing satisfaction with the meeting process and decreasing arguments over issues. Despite the many advantages of BIM tools, however, many design coordination issues remain undetected, design issues are poorly documented, and coordination strategies are inefficient. The objective of this study was to develop a characterization of the BIM building design coordination process, identify the bottlenecks in the current process, and provide design considerations to alleviate the bottlenecks. The bottlenecks include: outdated BIM, disconnected trades, lack of terminology, insufficient documentation, inefficient transitions across views and artifacts, unavailability of design information, information discrepancy, unfit navigation tools, and office–site disconnect. The outcomes of this research is useful for future construction projects and the software development community.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.446

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 head0.008
GPT teacher head0.176
Teacher spread0.168 · 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 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

Citations22
Published2019
Admission routes2
Has abstractyes

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