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Prediction Models for Structural Deterioration of Urban Drainage Pipes Using Ordered Probit and Neuro-Fuzzy Techniques

Tran, D. H and Ng, A. W. M and Perera, B. J. C (2008) Prediction Models for Structural Deterioration of Urban Drainage Pipes Using Ordered Probit and Neuro-Fuzzy Techniques. In: Proceedings of Water Down Under 2008: incorporating 31st Hydrology and Water Resources Symposium, and, 4th International Conference on Water Resources and Environment Research, 14-17 April, 2008, Adelaide, Australia. Lambert, Martin and Daniell, Trevor M and Leonard, Michael, eds. Engineers Australia, Modbury, SA, pp. 76-86.

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Abstract

Predicting structural deterioration of drainage pipes for storm water runoff is an important part of drainage maintenance programs. The structural deterioration is affected by various factors such as pipe size, age and soil type, among other variables and is defined using three condition states, with one being good, two being fair and three being poor over the lifetime of pipes. In this paper, two prediction models using ordered probit (OPPM) and neuro-fuzzy (NFPM) are developed for predicting the structural condition of urban drainage pipes. These two models are compared against each other by using the Goodness-of-fit test and two scalar performance measures, namely, overall success rate (OSR) and false negative rate (FNR). The predictive performance of OPPM might be affected by the noisy data owing to the nature of its statistical structure. The noisy data are inherent with the condition monitoring and assessment of the structural deterioration (i.e. collapsing into three condition states) and the vagueness of the input factors. This is what the adaptive neuro-fuzzy inference system (ANFIS) reportedly can handle well. The ANFIS is based on two powerful artificial intelligence techniques, multi-valued logical system (fuzzy logic) to account for noisy data and neural networks to map input factors to accurate outputs (i.e. structural condition). A case study was used to demonstrate the applicability of OPPM and NFPM. The results showed that the NFPM was more suitable for modelling structural deterioration of storm water pipes as substantiated by the Goodness-of-fit test. The NFPM consistently outperformed the OPPM in the train and test data set, however while these results are promising, further improvement is required before it can be used as a predictive model without additional filed expert opinion and confirmation

Item Type: Book Section
ISBN: 9781622764310 (print) 0858257351 (CD-ROM)
Uncontrolled Keywords: ResPubID14864, storm sewers, fuzzy systems, neural networks, industrial applications, drainage pipes, testing, goodness-of-fit tests
Subjects: Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0905 Civil Engineering
SEO Classification > 9004 Water and Waste Services
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Depositing User: VUIR
Date Deposited: 11 Jun 2014 06:49
Last Modified: 04 Feb 2015 03:07
URI: http://vuir.vu.edu.au/id/eprint/6271
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