MS 11

Novel Data Science for Disaster Prevention and Resilience of Civil Infrastructures


Takeshi Kitahara,

Kanto Gakuin University

Yoshikazu Takahashi,

Kyoto University

Michael Beer,

Leibniz University Hannover

Masaru Kitahara,

Leibniz University Hannover


JSCE (Japan Society of Civil Engineers) and ASCE (American Society of Civil Engineers) have jointly developed a framework of infrastructure resilience (IRF: Infrastructure Resilience Framework*) to draw a sketch map to see how components related to infrastructures are interrelated and connected with the resilience of the community. The utility of IRF discussed at the Joint Japan-US Symposium on Assessment, Management, and Governance for Infrastructure Resilience which held in April 2021. Therein, the concept of ‘resilience’ has advocated the importance of in-advance preparedness to bear infrastructure disruptions events and to recover as quickly as possible or even to build back better.

In this MS, based on those discussions about the concept of IRF, we would like to be expected to deeply considering novel data science methodologies, which could enhance disaster prevention and resilience of civil infrastructures against natural disasters. Especially, in the environment that natural disasters (such as earthquakes, Tsunami, floods, typhoons, and so on) have been becoming extremely serious, it is very meaningful to discuss these themes. The MS welcomes relevant research from academic, government, and industry sectors. The invited topics include but are not limited to data analytics, big data, system identification, meta-model, data-driven approach, etc.