BIM to Facilities Management: Presenting a Proven Workflow for Information Exchange
Bibliographic record
Abstract
Large institutional owners face a daunting task of assessing the condition of their facilities and forecasting maintenance and replacement costs of these assets. Computerized systems, generally known as asset management systems (AMS) are emerging in the market that provide maintenance budget forecasts based on building inventory data and conditions assessment. The main challenge that large institutional owners face is how to efficiently develop asset inventories that form the basis for AMS calculations. In response, this paper presents a research project wherein the research team developed a workflow for building information modeling (BIM) data transfer to AMS used for facility management (FM). This work provides a way for owners to leverage legacy BIM data to create building inventories or to specify BIM deliverables that will provide asset data for their intended AMS. This paper presents applied action research that developed a BIM to FM-AMS workflow as a proven practice that leads to sustainable facility management. The study results indicate that information exchange should require classification systems to utilize a consistent language between BIM systems. In addition, this paper presents peer institutional efforts and customization of classification systems that were studied to present a data crosswalk between these systems. The study results are expected to support owners and facility managers to understand the logic behind BIM information exchange practices so that they can adopt the best practice that fits the needs and goals of their organization.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".