ABSTRACT
Reliable, effective functioning of structures and infrastructure systems during and following hazard events, like earthquakes, hurricanes, and floods, is essential to public safety, economic vitality and quality of life. Risk-informed decisions that promote infrastructure resilience (or its ability to withstand, adapt and recover) require confident predictions of system performance when exposed to such stressors throughout its lifetime. However, this future brings uncertainties regarding dynamic, evolving conditions; challenges with respect to a legacy of disparate impacts of natural hazards and infrastructure (under)investment; and opportunities related to smart systems and emerging data and algorithms. This paper discusses a paradigm shift toward smart and objective life-cycle resilience modeling of infrastructure exposed to multiple hazards. We discuss the characteristics and dimensions of such a modeling framework intended to infuse intelligence and promote confident, unbiased predictions with respect to the algorithms used for infrastructure resilience pursuits. Case studies across hazards, systems and scales are leveraged to highlight recent advances in risk and resilience modeling from the structure to infrastructure to community scale.
