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Record W2313374887 · doi:10.1061/41000(315)6

Implementation of Building Information Modeling (BIM) on the Renovation of the Art Gallery of Alberta in Edmonton, Alberta

2008· article· en· W2313374887 on OpenAlexaboutno aff
Derrick Roorda, Mei Kuen Liu

Bibliographic record

VenueStructures Congress 2008 · 2008
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsBuilding information modelingLEAPSArchitectureArgument (complex analysis)Architectural engineeringUpgradeExhibitionComputer scienceInformation modelEngineeringSoftware engineeringBusinessOperations managementVisual arts

Abstract

fetched live from OpenAlex

There is little argument that Building Information Modeling is the way of the future in the AEC industry; however, few would freely admit that the future is not here yet for the type of the building projects that the current wave of architecture is pushing for. It is interesting to note that for most of the conferences and presentations widely available on this hot topic, few negative opinions are voiced. We found the article by Michael Tardif, titled "Faith-based BIM," to be one of the few such articles. While with each release of the BIM upgrade, the programs improve by leaps and bounds, as of the time of writing it is still very cumbersome, inaccurate, if not plain impossible, to model more complex geometry so prevalent in modern architecture, as so noted by the authors' experience, as well as Dr. Lachmi Khemlani's honest review on the AECbytes website. Of equal, if not more, importance is the lack of standard and clear delineation of liability in the building information model between the design and construction team. For an ideal project to be conceived in BIM, there needs to be a high level of trust between various parties involved that are unfortunately not commonly found in the current atmosphere of the industry. A series of surveys commissioned anonymously by Adobe Systems in 2004 and 2006 revealed an interesting finding that was covered in Sara Ferris' article on the Cadalyst. Amid the advancement and widespread availability of computing power, the AEC industry is relying on paper more than before: CAD files are reviewed in paper format more often in 2006 than in 2004. In 2004, 33% of the responders reviewed CAD files solely in electronic format, and that number dropped to 23% by year 2006. This apparent regression of acceptance in technology was commonly attributed to concerns about document security, with a substantial size of the responders sharing documents in noneditable digital format (e.g. TIFF or PDF) at 44%, and with another 37% of the survey participants providing paper copies only. While this particular survey did not cover the topic of BIM and the sharing of data in a central model, one gets an idea of what kind of comfort level the industry is currently operating in and thus the resistance one can expect for the implementation of BIM across not just the offices of the various design consultants, but more importantly, between the design team and the construction team, and ultimately, given to the building users for long term maintenance. For our project, the design team is comfortable and has prior experience with sharing information electronically for construction. However, the decision was made that only Rhinoceros files were released to the construction team for geometry control. The BIM files were only used to produce 2D construction drawings and were not released as part of the construction document due to unclear liability issues. At the time of writing, all eyes are on the National Institute of Building Sciences on their National Building Information Model Standard project. As part of the buildingSMART Initiative, this committee is charged with the follow standards: BIM scope, Coverage of Version, Reference Standards, Business Processes, Business Rules, Data Structures and Models, Implementation Guidance, and Maturity Model. It is the hope that with better definition of BIM, the AEC industry can have a better understanding of the risk involved in its implementation and thus be able to better manage the associated risk and make an informed decision about its adoption.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.003

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 head0.225
Teacher spread0.214 · 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 designObservational
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

Citations12
Published2008
Admission routes1
Has abstractyes

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