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Automated construction of variable density navigable networks in a 3D indoor environment for emergency response

Boguslawski, Pawel; Mahdjoubi, Lamine; Zverovich, Vadim; Fadli, Fodil

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Authors

Fodil Fadli



Abstract

© 2016 Elsevier B.V. Widespread human-induced or natural threats on buildings and their users have made preparedness and rapid response crucial issues for saving human lives. The ability to identify the paths of egress during an emergency is critical for rescue and emergency services. Quality models supporting real, or near-real, time decision making and allowing the implementation of automated methods are very important. In this paper, we propose a novel automated construction of the Variable Density Network (VDN) for determining egress paths in dangerous environments. VND is used for deriving a navigable network in an indoor building environment, including a full 3D topological model. The accuracy of the proposed paths prediction tool was compared with key methods for navigable network generation, using the actual floor plan of Doha World Trade Centre. Findings revealed that in comparison to prevailing approaches, a key benefit from this approach is an increased prediction accuracy of egress route planning.

Citation

Boguslawski, P., Mahdjoubi, L., Zverovich, V., & Fadli, F. (2016). Automated construction of variable density navigable networks in a 3D indoor environment for emergency response. Automation in Construction, 72(2), 115-128. https://doi.org/10.1016/j.autcon.2016.08.041

Journal Article Type Article
Acceptance Date Aug 24, 2016
Online Publication Date Sep 3, 2016
Publication Date Dec 1, 2016
Deposit Date Aug 24, 2015
Publicly Available Date Sep 3, 2017
Journal Automation in Construction
Print ISSN 0926-5805
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 72
Issue 2
Pages 115-128
DOI https://doi.org/10.1016/j.autcon.2016.08.041
Keywords navigable networks, indoor navigation, emergency response, 3D modelling, topological models
Public URL https://uwe-repository.worktribe.com/output/905347
Publisher URL http://dx.doi.org/10.1016/j.autcon.2016.08.041

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