A Real-time Path-Planning Model for Building Evacuations
Bibliographic record
Abstract
A Real-time Path-Planning Model for Building Evacuations Farid Mirahadi and Brenda McCabe Pages 998-1004 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Simultaneous evacuation is the most widely used evacuation strategy in buildings. However, there are other evacuation strategies that might lead to safer outcomes if selected appropriately. Different forms of evacuation result from applying time delays to phased evacuation or altering path planning. The best strategy for evacuation depends on the characteristics of the building and the circumstances of the particular emergency. A real-time evacuation path-planning model that identifies the fire hazard and proposes the best strategy of evacuation during the emergency can reduce risk and improve safety. In this paper, a model is proposed to find the safest strategy of evacuation based on the current state of the building and the emergency case. The model focuses on fire emergencies, as they are the dominant cause of fatalities in buildings compared to other types of natural and manmade disasters. The proposed model first defines a risk factor for each compartment based on the location of fire and then calculates the lowest risk path using Dijkstra algorithm. The path-planning runs on the geometric network graph (GNG), which is generated from the IFC file of the building. Furthermore, unexpected events during evacuation, e.g. another source of fire, can force the system to search for another strategy. Herein, a model is designed to monitor the building in real-time and in case of any unexpected event, changes the evacuation plan accordingly. The case study shows that the proposed model for real-time evacuation management can significantly enhance the safety level of evacuation compared to the conventional simultaneous evacuation process. Keywords: Evacuation; Dijkstra; Route risk index; Geometric network graph; BIM; IFC; Fire safety DOI: https://doi.org/10.22260/ISARC2019/0133 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".