Random disruptions resulting in loss of functionality in service legs or intermodal terminals of transportation networks are an inevitable part of operations, and considering the crucial role of aforementioned networks, it is prudent to strive towards avoiding high-consequence disruption events. The magnitude of the negative impact of a disruption is dependent on component criticality; therefore, limited resources of disruption mitigation should be assigned to the infrastructure with the highest priority. However, categorizing the service legs and terminals based on their actual post-disruption impact is computationally heavy and inefficient. We propose a methodology based on the combination of a bi-objective hazmat shipment planning optimization model and machine learning to identify critical infrastructure more efficiently. The proposed methodology is applied to part of CSX Corporation’s intermodal rail-truck network in the United States as a realistic size problem instance, in order to gain managerial insight and to evaluate the performance of the methodology. / Thesis / Master of Science (MSc)