Sensitivity of Markov Model to Different Sampling Sizes of Condition Data
Publication: Journal of Performance of Constructed Facilities
Volume 30, Issue 4
Abstract
After many years of service, constructed infrastructure facilities including drainage pipes, bridges, and roads show sign of deterioration. To ensure public safety and efficient management of these crucial assets, condition monitoring and mathematical predictive models have been widely used. Despite the advance in condition-monitoring techniques, closed-circuit television (CCTV) and expert-based inspection technique are still commonly used owing to their ease of use, productivity, and lower cost. Utilizing those condition data, Markov models have been widely used as a predictive tool for asset management of constructed infrastructure facilities. However, the sensitivity of the Markov model to different sampling sizes of condition data has not been investigated. This has a practical implication as more owners of infrastructure facilities start to collect condition data and are interested in understanding current and future deterioration of their infrastructure assets. This study addresses this knowledge gap with a case study of stormwater pipes. The results of the case study show that Markov models are sensitive to the sampling size of condition data. A sampling size between 600 and 700 data points is recommended for industry to collect condition data since it could provide a good starting view on deterioration patterns of stormwater pipe networks while suffering from a less than 10% error rate.
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Acknowledgments
Constructive comments from the Editor and Anonymous Reviewers are appreciated. The author is grateful to the kind support from Mr. Dominic Di Martino of Brimbank City and Mr. Martin Wong of the City of Greater Dandenong.
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Information & Authors
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Published In
Journal of Performance of Constructed Facilities
Volume 30 • Issue 4 • August 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Nov 17, 2014
Accepted: Jul 22, 2015
Published online: Sep 14, 2015
Discussion open until: Feb 14, 2016
Published in print: Aug 1, 2016
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