Data-Driven Modeling for Hazard-Resilient Infrastructure in Southern Lake Michigan Communities

Major Goals and Objectives

During the given one-year research period, I plan to develop a data-driven model integrating the physical model of infrastructure vulnerable to hazard and artificial intelligence machine learning algorithms to offer precautions and suggestions to resist natural hazards and enhance infrastructure flood resilience for the southern Lake Michigan communities. The proposed research targets to provide coastal communities with on-time and accessible suggestions to resist flooding attacks, support coastal industrial development without interference, give organizations reasonable, efficient recommendations to minimize the flooding impact on infrastructure, and offer the government customized design advice for infrastructure in the southern Lake Michigan region. Most importantly, this research will call public attention to the resilience of coastal communities and infrastructure.

Accomplishments / Benefits

Story: Meet our Grad Student Scholars: Junyi Duan

Research Information

Principal Investigator:
Junyi Duan
Initiation Date:
Purdue University


Chengcheng Tao
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