The Benefits of and Barriers to BIM Adoption in Canada
Bibliographic record
Abstract
The Benefits of and Barriers to BIM Adoption in Canada Yuan Cao, Li Hao Zhanga, Brenda McCabe and Arash Shahi Pages 152-158 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: The adoption of Building Information Modelling (BIM) has influenced the traditional methods of planning, design, construction and operation of a physical asset. Organizations in Canada have adopted BIM to improve designs, foster stakeholder collaboration, and facilitate construction processes. To understand the extent of BIM adoption and implementation in the industry, the University of Toronto Building Tall Research Centre conducted two annual BIM surveys. The 2018 survey, which was conducted in collaboration with tBIMc, focused on the Greater Toronto Area. In 2019, the survey was expanded nation-wide with support from Canada BIM Council, BuildingSMART Canada, and local BIM chapters. In this paper, the results of the 2019 nation-wide survey are presented and benchmarked against those in the 2018 survey. An in-depth discussion of the perceived benefits of and barriers to adopting BIM in Canada are also provided. This study serves as one of the milestones of the BIM transition process in Canada and aims to present a detailed view of the role that BIM plays in the future of the industry. Keywords: Building Information Modelling; BIM; survey; benefits; barriers; benchmark; DOI: https://doi.org/10.22260/ISARC2019/0021 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".