A methodology to integrate maintenance management systems and BIM to improve building management
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
Facility management (FM) teams routinely deal with numerous tasks, tools, and data sources to ensure the buildings they manage function properly. This diversity and the organizational complexity of these teams increase operational expenses related to interoperability. To address this, a methodology integrating BIM with maintenance management system logs is investigated herein. It includes two novel ways of automatically exchanging and visualizing such data using BIM as a common data environment. They provide building operators with greater context to expedite the decision-making process. A sequence diagram based on a typical facility management organization illustrates that these tools improve data exchange efficiency by which the operators understand the data and target operational improvements better. This methodology reduces the dependency on external programming languages for data processing. A text mining workflow is leveraged to process the work order (WO) descriptions. The methodology is demonstrated using a case study, which indicates that only 47 out of 81 rooms have one or more WOs and over 60% of all WOs are related to five rooms on the top floor. By focusing on these spaces, the underlying reasons and patterns of the faults were identified, which enhances the productivity of FM teams, occupants, and energy efficiency.
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 | 0.001 |
| 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