Collaborative BIM-Based Markerless Mixed Reality Framework for Facilities Maintenance
Bibliographic record
Abstract
Facilities maintenance tasks require gathering and sharing large amounts of information related to facilities components. This information covers historical inspection data and operation information. Despite the availability of sophisticated Computerized Maintenance Management Systems (CMMSs), these systems focus on the data management aspects (i.e. work orders, resource management and asset inventory) and lack the functions required to facilitate data collection and data entry, as well as data retrieval and visualization when and where needed. Building Information Modeling (BIM) provides opportunities to improve the efficiency of CMMSs by sharing building information between different applications/users throughout the lifecycle of the facility. This paper proposes a framework for a collaborative BIM-based Markerless Mixed Reality (BIM3R). The framework integrates CMMS, BIM, and video-based tracking in a BIM3R setting to retrieve information based on time (e.g. inspection schedule) and the location of the user, visualize maintenance operations, and support collaboration between the field and the office to enhance decision making. Finally, a prototype system is implemented and a case study is applied to demonstrate the feasibility of the proposed approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".