A functionality-based methodology for ranking subway systems for rehabilitation

Subway networks are safety-critical assets that should be studied in depth since their failure may cause catastrophic consequences. The current practice adopted by most transit authorities is prioritizing subway stations for rehabilitation based on structural needs. This classification is reflective of the station physical condition, yet, it neglects functional aspects like the expected impacts of failure and the station criticality. This paper presents a novel methodology of clustering subway stations based on a functional network classification. The network is broken down into its building blocks of systems, subsystems, and components. Different expected consequences of failure and station criticality attributes are identified and measured. The consequences of failure index measures the multi-perspective expected consequences of failure against financial, economical, and social perspectives. The criticality index measures the respective station criticality derived from its characteristics with respect to the network. A qualitative approach is adopted to assess the model attributes using an online questionnaire survey. The fuzzy analytic network process is used to deal with the imprecision and uncertainty associated with mapping of an expert's judgment to a crisp number. The two indices are combined in an integrated functionality index. The proposed framework helps authorities prioritize stations for rehabilitation based on a functional view that is often neglected for a more robust asset analysis. The methodology is applied to a network segment composed of six stations to validate the model. The model output is an indexed ranking of stations for rehabilitation together with the expected monetary consequences of failure. This research will help decision makers prioritize stations and elements across stations for rehabilitation based upon the expected integrated functionality index.