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Application of Probabilistic Neural Networks in Modelling Structural Deterioration of Stormwater Pipes

Tran, D. H and Ng, A. W. M and Perera, B. J. C and Burn, Stewart and Davis, P (2006) Application of Probabilistic Neural Networks in Modelling Structural Deterioration of Stormwater Pipes. Urban Water Journal , 3 (3). pp. 175-184. ISSN 1573-062X

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Abstract

In Australia, when stormwater systems were first introduced over 100 years ago, they were constructed independently of the sewer systems, and they are normally the responsibility of the third level of government, i.e., local government or city councils. Because of the increasing age of these stormwater systems and their worsening performance, there are serious concerns in a significant number of city councils regarding their deterioration. A study has been conducted on the structural deterioration of concrete pipes that make up the bulk of the stormwater pipe systems in these councils. In an attempt to look for a reliable deterioration model, a probabilistic neural network (PNN) model was developed using the data set supplied from participating councils. The PNN model was validated with snapshot-based sample data, which makes up the data set. The predictive performance of the PNN model was compared with a traditional parametric model using discriminant analysis on the same data set. Structural deterioration was hypothesised to be influenced by a set of explanatory factors, including pipe design and construction factors-such as pipe size, buried depth-and site factors-such as soil type, moisture index, tree root intrusion, etc. The results show that the PNN model has a better predictive power and uses significantly more input variables (i.e., explanatory factors) than the discriminant model. More importantly, the key factors for prediction in the PNN model are difficult to interpret, suggesting that besides prediction accuracy, model interpretation is an important issue for further investigation.

Item Type: Article
Uncontrolled Keywords: deterioration model, probabilistic neural networks, stormwater pipes, discriminant analysis
Subjects: RFCD Classification > 310000 Architecture, Urban Environment and Building
RFCD Classification > 290000 Engineering and Technology
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: Mr Angeera Sidaya
Date Deposited: 22 Feb 2007
Last Modified: 10 Jan 2017 11:52
URI: http://vuir.vu.edu.au/id/eprint/583
DOI: 10.1080/15730620600961684
ePrint Statistics: View download statistics for this item
Citations in Scopus: 11 - View on Scopus

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