BIM for Facilities Management: An Investigation into the Asset Information Delivery Process and the Associated Challenges
Bibliographic record
Abstract
The most common problem facility managers face is information accessibility. While BIM has been posited as a potential solution to increase the quality and availability of asset information to support facilities management (FM), few studies have captured the challenging aspects of developing and delivering this information within the context of real-world projects with owner-defined information requirements. Based on three longitudinal ethnographic case studies that included a set of comprehensive and formal information requirements within the supply contracts, this research contributes to a better understanding of the BIM-enabled asset information delivery process and its challenges by (1) characterizing the process in eight main activities with examples, and (2) mapping the challenges of using BIM for FM that have been identified in the literature and establishing connections between them. The results demonstrate that even with the early involvement of owners through the development of information requirements, several challenges still prevent owners from taking full advantage of BIM. There is still a limited understanding of how BIM can effectively support existing FM activities and how it impacts current design and construction processes in practice, which compromises the definition of clear and efficient information requirements. In that sense, the support provided by industry standards and guidelines remains limited. The contextualized understanding of the proposed BIM-enabled asset information delivery process and its challenges will help owners and facility managers with the decision-making process regarding the development of their information requirements, preventing inefficiencies and unrealistic expectations.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".