Adoption and Implementation of BIM in Canadian Construction Projects: Benefits, Challenges, and Limitations
Bibliographic record
Abstract
Adoption and implementation of the building information modeling (BIM) in Canada has been slower than other developed countries such as United States and United Kingdom. While benefitting from BIM, project owners and other key stakeholders in construction industry are faced with complications to accommodate BIM process in construction projects. This study was conducted to help improving the BIM process in Canadian construction industry by identifying current benefits, challenges, and limitations of implementing BIM in construction projects. A literature search was conducted on the BIM concept, its important aspects, BIM benefits, and main challenges in adoption and implementation of BIM. Subsequently, an online survey was designed and distributed to the BIM experts in Canadian construction industry. In addition, semi-structured interviews conducted to incorporate different perspectives of BIM professionals from architecture, engineering, construction, owners, and operations (AECOO) organizations. Accordingly, functional and performance benefits of efficient BIM implementation were identified and ranked with respect to the participants’ organizations. Similarly, main organizational challenges and technical issues in adoption and implementation of BIM were identified and discussed in detail. The results of this study can be used as a basis for further research to propose effective solutions for maximizing the benefits and minimizing the risks of implementing BIM in construction projects.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".