Spatial Sampling with Fisher Information for Optimal Maintenance Management and Quality Assurance
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 143, Issue 10
Abstract
Maintenance management has been relying heavily on collecting asset condition information to plan for maintenance activities and budget allocation. Data collection is often conducted on a sampling basis because of resource constraints. There is thus a perceived need for the development of an effective sampling framework that can determine statistically representative samples, reflect the true level of maintenance (LOM) at state/region/station levels, and accommodate agencies’ requirements. The paper advances existing knowledge by presenting a systemic approach for a sampling scheme development to assist maintenance activity planning. The proposed method addresses how much and where the agencies need to collect asset condition data for accurate LOM estimation. The method integrates Fisher information with a spatial sampling technique that can be customized based on local agencies’ requirements, such as station balanced, spatially balanced, or others. The framework is showcased via an example application of the Signage Repair and Replace database maintained by the Utah Department of Transportation (UDOT). Four sampling methods that might be tempered to various needs are implemented. Sampling results are presented and compared against historical full asset inventory via similarity analysis. The proposed framework lays a strong theoretical foundation for maintenance asset sampling and is effective for estimating LOM at state, region, and station levels to assist with budget allocation. The method can be easily transferable and adaptable to other agencies for optimal maintenance management.
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Acknowledgments
This paper is based on the research project entitled Statistical Analysis and Sampling Standards for Maintenance Management Quality Assurance (MMQA) jointly sponsored by the UDOT and the Mountain Plain Consortium (MPC) of the U.S. DOT University Transportation Center. Special thanks go to Rukhsana Lindsey, Lloyd Neeley, Tim Ularich, Jason Richins, Tammy Misarasi, Todd Richins, and Mark Marz for their support and feedback on this study.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 143 • Issue 10 • October 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jan 19, 2016
Accepted: May 5, 2017
Published online: Aug 2, 2017
Published in print: Oct 1, 2017
Discussion open until: Jan 2, 2018
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