Barriers, Strategies, and Best Practices for BIM Adoption in Quebec Prefabrication Small and Medium-Sized Enterprises (SMEs)
Bibliographic record
Abstract
Prefabricated construction has long faced problems due to the industry’s fragmentation. Building Information Modeling (BIM) has thus appeared as an efficient solution to provide a favorable environment for efficient completion of projects. Despite its benefits, implementing BIM successfully in small and medium-sized enterprises (SMEs), which represent the vast majority of manufacturers in Quebec, requires deep risk analysis and rigorous strategies. Hence, this work aims to study BIM implementation barriers, strategies, and best practices in wood prefabrication for SMEs through a literature review, semi-structured interviews, and an online survey. After qualitative content analysis, 30 critical barriers, 7 strategic milestones, and 31 best practices to maximize BIM benefits were revealed. One of the critical barriers concerns the effort required to develop BIM software libraries and programs to translate information from the BIM model to production equipment. Among the best strategies, it is essential to start by analyzing the current business model of the SMEs and to appoint a small BIM committee whose main responsibilities are management, coordination, and modeling. The prevalent best practices were to support the implementation team and encourage communication and collaboration. Previous studies show that BIM is not fully exploited in prefabrication for various reasons. This study highlights the critical barriers, strategies, and best practices for BIM adoption and proposes a framework for BIM implementation in prefabrication SMEs in Quebec, Canada. It also provides a summary of current knowledge and guidelines to promote BIM adoption in this sector.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".