Building Fragility Curves Based on Disaster-Victim Certificate Data of Multiple Local Governments Covering a Wide Range of Seismic Motion in the 2016 Kumamoto Earthquake

Torisawa, Kazuaki; Matsuoka, M.; Horie, Kei; Inoguchi, Munenari; Yamazaki, Fumio

doi:10.5610/jaee.21.5_98

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Utilizing the methods proposed by Hirokawa and Osaragi [52], we obtained disaster simulation data, including the probabilities of building collapse, street blockage, and fire outbreak. It is important to note that the parameters used for estimating building collapse probabilities are derived from the research conducted after the 2016 Kumamoto Earthquake and the 1995 Great Hanshin Earthquake [53], which are considered to be more accurate than previous studies. By employing these methods, we obtained probabilities of building collapse, road blockage, and fire spread as depicted in Figures 5-7.…”

Section: Data Generationmentioning

confidence: 99%

Multi-Objective Evcuation Planning Model Considering Post-Earthquake Fire Spread: A Tokyo Case Study

Tang,

Osaragi

2024

Sustainability

View full text Add to dashboard Cite

As an integral part of the 2030 Agenda for Sustainable Development, Disaster Risk Reduction (DRR) is essential for human safety and city sustainability. In recent years, natural disasters, which have had a tremendous negative impact on economic and social development, have frequently occurred in cities. As one of these devastating disasters, earthquakes can severely damage the achievements of urban development and impact the sustainable development of cities. To prepare for potential large earthquakes in the future, efficient evacuation plans need to be developed to enhance evacuation efficiency and minimize casualties. Most previous research focuses on minimization of distance or cost while ignoring risk factors. We propose a multi-objective optimization model with the goal of reducing the risk during the evacuation process, which is called the risk reduction model (RRM). Problem-specific indicators for screening optimal solutions are introduced. The research selects the Ogu area in Tokyo as a case study, where there is a relatively high density of wooden structures, increasing the risks of building collapse and fire spread after an earthquake, and is based on a two-phase evacuation flow that considers secondary evacuation for fire response. The results indicate that, in this case, RRM can, in most situations, reduce the risk level during the evacuation process and improve evacuation efficiency and success rate without significantly increasing the total evacuation distance. It proves to be superior to the traditional distance minimization model (DMM), which prioritizes minimizing the total distance as the objective function.

show abstract

Section: Data Generationmentioning

confidence: 99%