The synergy of non-manifold topology and reinforcement learning for fire egress

Jabi, Wassim ORCID: https://orcid.org/0000-0002-2594-9568, Chatzivasileiadi, Aikaterini ORCID: https://orcid.org/0000-0001-5413-466X, Wardhana, Nicholas, Lannon, Simon ORCID: https://orcid.org/0000-0003-4677-7184 and Aish, Robert 2019. The synergy of non-manifold topology and reinforcement learning for fire egress. Presented at: eCAADe (Education and Research in Computer Aided Architectural Design in Europe) + SIGraDi (Sociedad Iberoamericana de Gráfica Digital) 2019 Conference, Porto, Portugal, 13-15 September 2019. eCAADe SIGraDi 2019 Architecture in the Age of the 4th Industrial Revolution, Volume 2. , vol.2 eCAADe, pp. 85-94.

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Abstract

This paper illustrates the synergy of non-manifold topology (NMT) and a branch of artificial intelligence and machine learning (ML) called reinforcement learning (RL) in the context of evaluating fire egress in the early design stages. One of the important tasks in building design is to provide a reliable system for the evacuation of the users in emergency situations. Therefore, one of the motivations of this research is to provide a framework for architects and engineers to better design buildings at the conceptual design stage, regarding the necessary provisions in emergency situations. This paper presents two experiments using different state models within a simplified game-like environment for fire egress with each experiment investigating using one vs. three fire exits. The experiments provide a proof-of-concept of the effectiveness of integrating RL, graphs, and non-manifold topology within a visual data flow programming environment. The results indicate that artificial intelligence, machine learning, and RL show promise in simulating dynamic situations as in fire evacuations without the need for advanced and time-consuming simulations

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Architecture
Publisher:	eCAADe
ISBN:	9789491207181
ISSN:	2684-1843
Funders:	Leverhulme Trust
Projects:	Enhancing the representation of architectural space in 3D modelling environments
Date of First Compliant Deposit:	15 August 2019
Date of Acceptance:	1 June 2019
Last Modified:	12 May 2026 14:32
URI:	https://orca.cardiff.ac.uk/id/eprint/124949

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