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Record W101405778 · doi:10.1061/41020(339)52

Querying IFC-Based Building Information Models to Support Construction Management Functions

2009· article· en· W101405778 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConstruction Research Congress 2009 · 2009
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBuilding information modelingComputer scienceInformation modelOntologyFeature (linguistics)Product (mathematics)Software engineeringDomain (mathematical analysis)Systems engineeringFunction (biology)Engineering

Abstract

fetched live from OpenAlex

The design and construction community has shown increasing interest in adopting Building Information Models (BIM). While the richness of design information offered by BIM is evident, there are still tremendous challenges in getting construction-specific information out of BIM, particularly from IFC-based product models. This paper describes our approach for querying construction-specific design conditions from an IFC-based model. The approach involves: (1) the formalization of construction-specific design conditions as an ontology of product features, (2) the automated generation of feature-based product models for a particular construction domain and function, and (3) a formal specification that supports user-driven queries of the feature-based model. This approach allows practitioners to answer a broad range of user-customizable queries in support of different construction management functions. It also transforms designer-focused EFC-based models into construction-focused, feature-based models.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.296
Teacher spread0.261 · 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