Development of quality improvement procedures and tools for facility management BIM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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 it