  Ice storms are one of the most devastating natural hazards which have the
potential to inflict significant damage to the built environment. The
multi-hazard nature of ice events complicates the analysis of their induced
risk due to their inherent nonlinear interactions. In addition, the concurrent
and interacting hazards are often responsible for several
aerodynamical/dynamical instabilities such as the galloping mechanism.
Moreover, the existing risk approaches are usually not suited for large-scale
risk evaluation over extended geographical regions due to the involved
high-computational costs. Therefore, in this study, a novel data-driven
multi-scale performance-based ice engineering (PBIE) framework is developed to
support the design of new structures subjected to ice storms or the
rehabilitation of existing ones. In addition, the proposed PBIE is capable of
rapidly estimating the real-time risk over an extended region due to an ice
event. Specifically, it leverages the superior capabilities of state-of-the-art
data-driven techniques (e.g., machine learning) to efficiently generate the
corresponding risk maps and identify the high-risk areas. The proposed PBIE
framework is applied to a simplified example in which the galloping-induced
risk on iced conductors, in terms of the galloping amplitude, is evaluated for
both local and regional scales. The resulting PBIE framework can be readily
applied for design or retrofitting purposes or integrated within an emergency
response management system to inform preventive actions that can mitigate the
ice storm-induced damages and save lives.
