6 years have passed since the 2011 Great East Japan earthquake. Many new findings, insights and suggestions have been made and were implemented in disaster observation, sensing, simulation, and damage determination. The challenges for disaster mitigation against future catastrophic natural disasters, such as the Tokyo metropolitan earthquake and Nankai Trough earthquake, are how we share the visions of the possible impacts and prepare for mitigating the losses and damages, and how we enhance society’s disaster resilience. A huge amount of information called “disaster big data” obtained, which are related to the dynamic flow of a large number of people, vehicles and goods inside and outside the affected areas. This has dramatically facilitated our understanding of how our society has responded to the unprecedented catastrophes. The key question is how we use big data in establishing the social systems that respond promptly, sensibly and effectively to natural disasters, and in withstanding the adversities with resilience. Researchers with various expertise are working together under the collaborative project called JST CREST “Establishing the most advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation.” The project aims to identify possible disaster scenarios caused by earthquake and tsunami that occur and progress in a chained or compound manner and to create new technologies to lead responses and disaster mitigation measures that encourages the society to get over the disaster. This special issue titled “Disaster and Big Data Part 2,” including 13 papers, aims to share the recent progress of the project as the sequel of Part 1 published in March 2016. As an editor of this issue, I would like to express our deep gratitude for the insightful comments and suggestions made by the reviewers and the members of the editorial committee.
Special issue for the 13th world congress on structural and multidisciplinary optimization—editorial note