Exploring BIM Implementation Challenges in Complex Renovation Projects: A Case Study of UBC’s BRDF Expansion
Bibliographic record
Abstract
Renovation of existing buildings pose unique challenges to the projects, especially when facilities must remain operational during construction. Building Information Modeling (BIM) methods offer a potential solution by enhancing project management and coordination. Nevertheless, comprehensive case study research on BIM implementation challenges and benefits in renovation projects is lacking. This research addresses this gap through an ethnographic investigation of BIM implementation in a complex renovation project. The ethnographic methods involved direct observation of project meetings, active engagement in all project communications, and access to project data resources. Additionally, surveys and expert interviews with key decision-makers were conducted. The findings reveal how BIM implementation streamlined project management and improved communication, decision making, and output quality, despite limited prior BIM expertise among the major stakeholders. Challenges included a lack of BIM skills, absence of standardized practices, and unclear data management. Furthermore, valuable lessons were identified, including that the necessity of BIM requirements and proper procurement methods encompassing the entire project workflow, formalizing information exchange, preventing information fragmentation, facilitating model accessibility, and ensuring clarity in model detail and content are crucial for project success. This research sheds light on the potential of BIM in renovation projects and highlights key considerations for successful implementation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".