BIM for Facility Management: Design for Maintainability with BIM Tools
Bibliographic record
Abstract
As Building Information Modeling (BIM) becomes widely adopted by the construction industry, it holds undeveloped possibilities for supporting Facility Management (FM).Some FM information systems on the market claim to address the needs for FM requirement.However, the question of whether the functionalities provided by the current BIM-based FM software companies are those actually required by the FM Professionals still need to be answered.The data is required by FM professionals in the operation and maintenance phases of facilities and type of maintainability problems that frequently occur, which can be solved early in design phase, have not yet been addressed.The aim of this paper is to clarify the frequently occurring maintainability problems and to investigate the potential areas that can use BIM technology to solve the maintenance problems in early the design phase.A survey was conducted to collect perspectives from the industry practitioners for the maintenance problems and their frequency.The survey results indicated that maintainability considerations should be taken into consideration during the facility design phase.The results also address the perceived areas by practitioners that need maintainability consideration in design phase.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".