The Benefits of and Barriers to BIM Adoption in Canada
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it