Information Exchange Process for AR based Smart Facility Maintenance System Using BIM Model
Bibliographic record
Abstract
Information Exchange Process for AR based Smart Facility Maintenance System Using BIM Model Suwan Chung, Soonwook Kwon, Daeyoon Moon, K.H. Lee and J.H. Shin Pages 595-602 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: In this study, we propose information exchange process for the effective integration of building information modeling (BIM) into an augmented reality (AR)-based smart facilities maintenance (SFM) system. The proposed SFM system refers to a system that combines technologies such as AR and IoT sensors in the field maintenance work. This requires the acquisition of data from various sources followed by transformation of these data into an appropriate format. Construction operation building information exchange (COBie) is widely used as a means to effectively integrate and utilize information for maintenance. Therefore, SFM system has a requirement attribute information system with reference to COBie. But this information should be linked to the maintenance work procedures in the actual use case scenario and it is necessary to define the information exchange process. To solve this problem, we uses the following methods to enable SFM system development with applications for BIM and AR technologies in the FM of the building sector of public facilities. First, it analyzes the previous studies on BIM-based maintenance works and AR technology. Second, it divides the SFM work process utilizing the BIM-based COBie system, and it defines the COBie data required for each work phase. Third, it develops a scenario-based business process modeling notation (BPMN) for the SFM system prototype. Finally, it proposes an implementation method of SFM system architecture. Keywords: Building information model; Facility maintenance; Augmented reality; Business process modeling notation DOI: https://doi.org/10.22260/ISARC2019/0079 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".