A Building Information Management (BIM) Framework and Supporting Case Study for Existing Building Operations, Maintenance and Sustainability
Bibliographic record
Abstract
Building Information Management (BIM) models are transforming how buildings are designed and constructed, and can facilitate multi-disciplinary coordination, and integrate 3D design, analysis, cost estimating, and construction scheduling. By extending the model into the post-occupancy period, BIM models can be used to support Facilities Management and Building Operations, and offer a consolidated interface for information regarding all aspects of building operational performance. Four key challenges must be overcome to develop BIM models suitable for Sustainable Operations management: (1) identification of critical information required to inform Operational decisions, (3) the high level of effort to create new or modify existing BIM models for the building(s), (2) the management of information transfer between real-time operations and monitoring systems and the BIM model, and (4) the handling of uncertainty based on incomplete building documentation. This paper describes the process used to addresses and overcome each of these challenges. The BIM framework and its refinement are presented along with evaluative data from a case study where a model was developed using this framework for a complex university building. The results of this study demonstrate how these BIM models can be developed for the most challenging existing building scenarios and effectively used to improve building management and performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".