Extending IFC for Fire Emergency Real-Time Management Using Sensors and Occupant Information
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
The increasing complexity of buildings has brought some difficulties for emergency response. When fires occur in a building, limited perception regarding the disaster area and occupants can increase the probability of injuries and damages. Thus, the availability of comprehensive and timely information may help understand the existing conditions and plan an efficient evacuation. For this purpose, Building Information Modeling (BIM) should be integrated with three sets of information: (1) occupancy that defines the type of space usage; (2) occupants’ information; and (3) sensory data. The Industry Foundation Classes (IFC), as a standard of BIM, has the definitions for all areas, volumes, and elements of a building. IFC also has the basic definitions of sensor and occupant entities. However, these entities do not provide enough dynamic and accurate information for supporting emergency management systems. This paper aims to extend IFC for fire emergency real-time management using sensors and occupants’ information. The specific objectives of this paper are: (1) extending IfcSensor entity for occupant’s sensors; (2) adding new attributes to IfcOccupant to support emergency response operations and defining a new entity for occupancy; and (3) defining the relationships between sensors, occupants, occupancy, time series, and building components in the context of building evacuation. The feasibility of the proposed method is discussed using a case study.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it