Development of quality improvement procedures and tools for facility management BIM
Bibliographic record
Abstract
Despite the potentially significant benefits that Building Information Modeling (BIM) can offer during a facility's operation and maintenance (O&M) phase, the construction industry has thus far mainly implemented BIM in the design and construction phases. This is because even though as-built BIM models are delivered at the handover stage, owners and operators rarely have the expertise to efficiently use and update them. Additionally, industry standards do not provide precise guidelines on aspects such as the ease of use, interoperability, and maintainability of FM-BIM, that could ensure their efficient and effective utilization. Moreover, given that these models are mainly developed for the design and construction phases, they usually contain design and construction details that are not useful for the building's operation and maintenance or lack information required for this phase. Thus, this paper investigates correspondences between as-built models and O&M requirements, using procedures and semi-automated tools to facilitate quality management activities for FM-BIM. To achieve this, a detailed checklist of items that are required in the BIM models at the handover stage and of the items that can be purged was created. This checklist is part of an overall quality framework that includes quality assurance and quality control tasks to deliver useable models for the operation and maintenance phase. Additionally, a procedure and a set of tools were investigated to semi-automatically apply a collection of the items of the checklist on as-built models. A process flow is presented to assist in quality management activities during the development of the models and to prepare them for handover. Finally, two case studies were conducted to verify and validate the applicability of the developed tools and proposed procedures.
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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 | 0.029 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".