AI-Enabled Disaster Assessment and Resilience for Interdependent Critical Civil Infrastructures

H. Pan, Y. Ge, Z. Lin
North Dakota State University, United States

Keywords: Deep learning, AI, remoste sensing, damage conditonal assessment, structural health monitoring

In recent years, several similar major catastrophic disasters associated to critical infrastructures, including oil train derailment and explosion, pipeline spills/explosions, have raised more widespread attention that posts high risk to people’s life, and surrounding environments. Critical civil infrastructures have usually been confronting with the challenges of efficient preparation for and timely response to a range of natural hazards, and suffering from enormous adverse socioeconomic consequences. Therefore, this study aimed to gain understanding of the underlying dynamic response of these civil infrastructures to damage using AI-enabled data analytics through remote sensing data sets and computer vision and uncover uncertainties for probabilistic disaster assessment through the developed computation intelligence for future artificial intelligence structures.