Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128167
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Type: Journal article
Title: Multi-stage parameter-constraining inverse transient analysis for pipeline condition assessment
Author: Zhang, C.
Zecchin, A.
Lambert, M.
Gong, J.
Simpson, A.
Citation: Journal of Hydroinformatics, 2018; 20(2):281-300
Publisher: IWA Publishing
Issue Date: 2018
ISSN: 1464-7141
1465-1734
Statement of
Responsibility: 
Chi Zhang, Aaron C. Zecchin, Martin F. Lambert, Jinzhe Gong and Angus R. Simpson
Abstract: Fault detection in water distribution systems is of critical importance for water authorities to maintain pipeline assets effectively. This paper develops an improved inverse transient analysis (ITA) method for the condition assessment of water transmission pipelines. For long transmission pipelines ITA approaches involve models using hundreds of discretized pipe reaches (therefore hundreds of model parameters). As such, these methods struggle to accurately and uniquely determine the many parameter values, despite achieving a very good fit between the model predictions and measured pressure responses. In order to improve the parameter estimation accuracy of ITA applied to these high dimensional problems, a multi-stage parameter-constraining ITA approach for pipeline condition assessment is proposed. The proposed algorithm involves the staged constraining of the parameter search-space to focus the inverse analysis on pipeline sections that have a higher likelihood of being in an anomalous state. The proposed method is verified by numerical simulations, where the results confirm that the parameters estimated by the proposed method are more accurate than the conventional ITA. The proposed method is also verified by a field case study. Results show that anomalies detected by the proposed methods are generally consistent with anomalies detected by ultrasonic measurement of pipe wall thickness.
Keywords: Condition assessment; identifiability; inverse transient analysis; water pipelines
Rights: © IWA Publishing 2018
RMID: 0030085805
DOI: 10.2166/hydro.2018.154
Grant ID: http://purl.org/au-research/grants/arc/DP140100994
http://purl.org/au-research/grants/arc/LP130100567
Appears in Collections:Civil and Environmental Engineering publications

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