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Record W2953892960 · doi:10.22260/isarc2019/0133

A Real-time Path-Planning Model for Building Evacuations

2019· article· en· W2953892960 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the ... ISARC · 2019
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency evacuationComputer scienceDijkstra's algorithmPath (computing)Motion planningSAFERHazardOperations researchEmergency managementShortest path problemGraphEngineeringComputer securityArtificial intelligenceGeographyRobot

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.243
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it