A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban risk assessment model to quantify earthquake‐induced elevator passenger entrapment with population heatmap
AbstractThe seismic resilience of cities plays a crucial role in achieving the United Nations Sustainability Development Goal. However, despite the occurrence of elevator passenger entrapment in numerous earthquakes, there is a notable lack of studies addressing this sophisticated issue. This study aims to bridge this gap by proposing a novel urban risk assessment model designed to evaluate city‐scale earthquake‐induced elevator passenger entrapment. The model integrates big data and physics‐based approaches. A novel mapping method was developed to estimate city‐scale elevator traffic level based on population heatmap data and deep learning. A process‐based parallel computing scheme was designed to accelerate the assessment. The applicability was demonstrated based on a real‐world urban area comprising 619 buildings. The findings reveal that as the time of the earthquake varies, the risk exhibits significant fluctuations. Additionally, this study highlights that a simplistic correspondence between seismic intensity and passenger entrapment risk can lead to erroneous estimations.
Urban risk assessment model to quantify earthquake‐induced elevator passenger entrapment with population heatmap
AbstractThe seismic resilience of cities plays a crucial role in achieving the United Nations Sustainability Development Goal. However, despite the occurrence of elevator passenger entrapment in numerous earthquakes, there is a notable lack of studies addressing this sophisticated issue. This study aims to bridge this gap by proposing a novel urban risk assessment model designed to evaluate city‐scale earthquake‐induced elevator passenger entrapment. The model integrates big data and physics‐based approaches. A novel mapping method was developed to estimate city‐scale elevator traffic level based on population heatmap data and deep learning. A process‐based parallel computing scheme was designed to accelerate the assessment. The applicability was demonstrated based on a real‐world urban area comprising 619 buildings. The findings reveal that as the time of the earthquake varies, the risk exhibits significant fluctuations. Additionally, this study highlights that a simplistic correspondence between seismic intensity and passenger entrapment risk can lead to erroneous estimations.
Urban risk assessment model to quantify earthquake‐induced elevator passenger entrapment with population heatmap
Computer aided Civil Eng
Gu, Donglian (author) / Zhang, Ning (author) / Xu, Zhen (author) / Wu, Yongjingbang (author) / Tian, Yuan (author)
Computer-Aided Civil and Infrastructure Engineering ; 39 ; 2204-2222
2024-07-01
Article (Journal)
Electronic Resource
English
Urban Earthquake Disaster Risk Assessment and Management
| British Library Conference Proceedings | 1999