Title
A hybrid hierarchical agent-based simulation approach for buildings indoor layout evaluation based on the post-earthquake evacuation
Abstract
In the aftermath of severe earthquakes, building occupants evacuation behaviour is a vital indicator of the performance of an indoor building design. However, earthquake evacuation has been systematically neglected in the current building design practice. Arguably, one of the primary reasons for this is that post-earthquake evacuation behaviour is complex and distinct from all other types of evacuation behaviours such as fire. Thus, a comprehensive approach to considering the integration of human evacuation behaviour and a building's indoor layout design, mainly focused on non-structural damage, has been consistently neglected in the literature. In this paper, a hierarchical hybrid Agent-Based Model (ABM) framework integrated with a Cellular Automata (CA) and a 2D Building Information Model (BIM) damage visualisation to consider an approximation of non-structural damage has been developed. The proposed ABM incorporates learning mechanisms and human psychological aspects influencing evacuees' utility during the navigation process. The proposed approach was verified by comparing the results to previous real-life post-earthquake evacuation data and a “model to model” comparison of results from the existing relevant studies. The model prototype was successfully tested to simulate the pedestrian evacuation process from one floor of the new engineering building at The University of Auckland, New Zealand. The proposed simulation approach has been carried out for two different internal layout design alternatives where five population sizes are evacuated through different scenarios. The outputs from this study can be used to improve the design's compatibility of the building's indoor layout with the occupants' post-earthquake evacuation behaviour.
Year
DOI
Venue
2022
10.1016/j.aei.2022.101531
Advanced Engineering Informatics
Keywords
DocType
Volume
Evacuation simulation,Indoor layout design evaluation,Agent-based models,Reinforcement learning
Journal
51
ISSN
Citations 
PageRank 
1474-0346
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Sajjad Hassanpour100.34
Vicente Gonzalez200.34
Jiamou Liu34923.19
Yang Zou400.34
Guillermo Cabrera-Guerrero500.34