This thesis proposes a novel working paradigm for transportation infrastructure asset management by viewing the transportation networks as key components (or nodes) of a broader network of resources, which includes infrastructure linked with society's ecological, social, and economic systems. An extensive review of network science literature suggested that to understand the behavior of a complex network is imperative to characterize its topology. Consequently, this thesis focused on developing a framework to characterize the topology of the transportation infrastructure systems, and understanding how the unveiling topology can be used for supporting transportation asset management decisions.

The proposed methodology determines whether the transportation infrastructure networks can be modeled as scale-free or exponential networks, using a framework for characterizing the agents of the network, their direct and indirect interactions among each other, and their importance as elements of a complex network, and utilizes these data to support transportation asset management. The methodology consist of seven steps: (1) define the networks of interest; (2) identify their intrinsic components; (3) visualize the identified networks using GIS maps; (4) identify direct and indirect interactions through superposition of the networks; (5) represent the relationship between the nodes and their linkages by frequency diagrams in order to determine the intrinsic topology of the network; (6) illustrate (graphically) the overall transportation infrastructure with the help of GIS; and (7) analyze the TINs from the decision-maker point of view, identifying the elements that are more relevant or need more attention on the network.

The procedure is then implemented in a small network in a localized area (Town of Blacksburg, Virginia) to show its practicality, and recommendations for further development and mathematical modeling in order to allow its implementation in larger networks are provided. Based on frequency analysis of the nodes and their connectivity, it was concluded that the transportation infrastructure networks in the case study behave as exponential networks. The study showed that the links determine how the infrastructure network grows and that problems like congestion can be addressed by analyzing other factors related with topology, such as speed, unit size, and lane width. The proposed methodology was found to be useful as an asset management tool. Finally, a list of findings and recommendations for further research are presented as opportunities to enhance the management of transportation infrastructure networks.