Adoption and Implementation of BIM in Canadian Construction Projects: Benefits, Challenges, and Limitations
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
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.
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 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 | 0.001 |
| 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