Toward Smart-Building Digital Twins: BIM and IoT Data Integration
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
Smart-building digital twins aim to virtually replicate the static and dynamic building characteristics through real-time connectivity between the virtual and physical counterparts. The virtual replica of the building can then be leveraged to monitor the current state, predict the future state, and take proactive measures to ensure optimal operation. Despite its potential, smart-building digital twin research is at an early stage compared to manufacturing and aerospace fields. One of the major impediments to adopting digital twin technology in smart buildings is the lack of interoperability, primarily between Building Information Modeling (BIM) and Internet of Things (IoT) data sources. Consequently, this paper presents a novel multi-layer digital twin architecture for smart buildings called BIM-IoT Data Integration (BIM-IoTDI) to enable semantic interoperability among smart-building digital twin applications. A detailed framework is presented based on the newly developed architecture, introducing an ontology-based query mediation method that provides integrated data access. An experimental evaluation model is developed to characterize the feasibility of the BIM-IoTDI architecture and framework. Furthermore, the performance of the new query mediation method is evaluated and compared to an existing BIM-IoT data integration approach. According to the evaluation results, the BIM-IoTDI architecture and framework are better suited to supporting the envisioned smart-building digital twin capabilities.
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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 | 0.000 | 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 | 0.001 | 0.003 |
| Open science | 0.001 | 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