Analysis of BIM use for asset management in three public organizations in Québec, Canada
Bibliographic record
Abstract
Purpose Given the ongoing digital transformation, building information modeling (BIM) has great potential to create a collaborative environment in the whole lifecycle of the built asset, from inception to decommissioning. The paper aims to discuss this issue. Design/methodology/approach This paper relates current developments in Québec with regard to the use of BIM for asset management (AM). The steps taken by three public organizations to develop their capabilities and take advantage of new possibilities are presented. The main methodological approach is based on participant observation, through case studies complemented by a questionnaire. Findings This paper reports on results and analysis of an important module of a broader research project on the impact of new technologies and collaborative methods for projects and AM. The results of this first research module points to the importance of using pilot projects to develop a continuous improvement approach, where feedback loops from projects support the development of AM capabilities and culture. Another important finding is the importance of sharing experience for the three public organizations involved. Originality/value The main contributions of this paper are to document this overarching research program and to gain deeper insights by reflexively considering the steps taken and the ones ahead for the quest to enhance the transfer of information for built assets at the end of projects to the operations and maintenance phase and to use BIM for operation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 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) | 0.004 | 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".