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Markov and Neural Network Models for Prediction of Structural Deterioration of Stormwater Pipe Assets

Tran, Huu Dung, Perera, B. J. C and Ng, A. W. M (2010) Markov and Neural Network Models for Prediction of Structural Deterioration of Stormwater Pipe Assets. Journal of Infrastructure Systems, 16 (2). pp. 167-171. ISSN 1076-0342


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Storm-water pipe networks in Australia are designed to convey water from rainfall and surface runoff. They do not transport sewerage. Their structural deterioration is progressive with aging and will eventually cause pipe collapse with consequences of service interruption. Predicting structural condition of pipes provides vital information for asset management to prevent unexpected failures and to extend service life. This study focused on predicting the structural condition of storm-water pipes with two objectives. The first objective is the prediction of structural condition changes of the whole network of storm-water pipes by a Markov model at different times during their service life. This information can be used for planning annual budget and estimating the useful life of pipe assets. The second objective is the prediction of structural condition of any particular pipe by a neural network model. This knowledge is valuable in identifying pipes that are in poor condition for repair actions. A case study with closed circuit television inspection snapshot data was used to demonstrate the applicability of these two models.

Item Type: Article
Uncontrolled Keywords: ResPubID19507, stormwater management, water pipelines, Markov process, probability, neural networks, structural failures, deterioration
Subjects: Current > FOR Classification > 0905 Civil Engineering
Historical > SEO Classification > 9004 Water and Waste Services
Historical > Faculty/School/Research Centre/Department > Institute for Sustainability and Innovation (ISI)
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 14 Nov 2011 04:32
Last Modified: 05 Jun 2018 07:19
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Citations in Scopus: 16 - View on Scopus

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