Statistical Data Analysis for Trackway Asset Management Using Low-Level Nonconformance Rates

Abstract

Well-documented conditions of the country’s subway systems suggest that the development of a management strategy for aging infrastructure is an important asset management challenge. The purpose of this work is to address this challenge by developing a statistical data analysis approach for forecasting the aging effect on the time-dependent deterioration rate of an identified trackway cluster. The infrastructure database is the New York City subway system and the trackway that supports the delivery of service. While much focus has been given to the steel rail subcomponent of the trackway, little has been done in the way of statistical analysis on the low-level nonconformances in the support structure of those rails—namely, the invert, ties, fasteners, and plates. Though detectable, these conditions pose no risk to either safety or the serviceability of that asset, yet they do provide a certain degree of awareness that an entirely intuitive lifecycle deterioration process is underway. This paper illustrates the development of a statistical data analysis methodology using available trackway data sets for forecasting the aging effect on the system performance.