MS Title:
Data-Driven Risk and Resilience Modeling of Infrastructure Against Catastrophic Hazards

Description:
With rapid urbanization and the concurrent development of built environment, the vulnerability and risk of infrastructure systems such as buildings, bridges, and power facilities to catastrophic hazards (e.g., earthquakes, floods, hurricanes, blasts, etc.) have become a major concern for both practitioners and stakeholders seeking thorough evaluation and management. In this regard, it is crucial to identify potential risks and vulnerabilities, and develop effective strategies for mitigating and recovering from catastrophic events. Addressing these challenges requires efficient and effective tools for risk prediction, inference, and recovery modeling of infrastructure systems against catastrophic events, accounting for their interdependency. Recent studies have demonstrated the promise of data-driven techniques such as machine learning and statistical modeling in addressing these issues. In essence, this special session aims to advance theories and techniques for data-driven risk and resilience modeling of infrastructure against catastrophic hazards. This session is expected to provide a platform for knowledge exchange in this rapidly evolving field with wide-ranging implications for urban planning, disaster management, climate change adaptation, and more.

Session Chairs:
Xiaowei Wang, Tongji University, Shanghai, China. E-mail: xiaoweiwang@tongji.edu.cn  
Ruiwei Feng, Hong Kong Polytechnic University, Hong Kong, China, E-mail: ruiwei.feng@polyu.edu.hk
Yaohan Li, Hong Kong Metropolitan University, Hong Kong, China, Email: yahli@hkmu.edu.hk
Yue Li, Case Western Reserve University, Cleveland, OH, USA. E-mail: yxl1566@case.edu