LAUSR

System availability for aging transportation infrastructure decreases in the absence of maintenance and modernization activities. This degradation is compounded when coupled with the growing backlog of needs and limited resources, making prioritization of these activities a complex problem. Current decision-making techniques utilized to solve this complex problem lack a holistic approach, objectivity, and topological aspect. To address these shortcomings, this research proposes a new comprehensive decision-making framework for maintenance and modernization of aging transportation infrastructure. The framework first employs a systems thinking approach to identify impact factors, then conducts a complex network analysis to assess the location criticality of each component within the system, and finally applies an innovative Bayesian network structure learning method to eliminate subjective judgment and reduce the drawbacks of currently available learning algorithms when using real-world data. The robustness of the proposed framework is demonstrated via a case study for inland waterways. Analysis of the results confirm the prioritization determined by utilizing the proposed framework optimizes system availability. This framework provides decision makers with an index number representing the need for maintenance and modernization of each project and a prioritized list in terms of essentiality.