Statistical Data Analysis for Trackway Asset Management Using Low-Level Nonconformance Rates
Publication: Journal of Infrastructure Systems
Volume 29, Issue 1
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.
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Data Availability Statement
The only data used in this study are the length, total nonconformances, and AGAs for each of the devices used in the analysis and are available upon reasonable request. The source data from which these attributes were derived are the property of the operating agency, referenced in the Acknowledgments Section, and were made available through a nondisclosure agreement. All statistical analysis was made using MS Excel spreadsheets, and all models are incorporated in the report.
Acknowledgments
The authors would like to thank the New York City Transit Authority, especially Chief Track Engineer Antonio Cabrera, and William Goess and Professor Ray Patel, from the New York University Tandon School of Engineering for their significant contributions to this work.
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Information & Authors
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Published In
Journal of Infrastructure Systems
Volume 29 • Issue 1 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 16, 2020
Accepted: Sep 14, 2022
Published online: Nov 24, 2022
Published in print: Mar 1, 2023
Discussion open until: Apr 24, 2023
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