Urban risk assessment model to quantify earthquake-induced elevator passenger entrapment with population heatmap

Abstract

The 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.