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Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models

Tran, H. D and Perera, B. J. C and Ng, A. W. M (2009) Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models. Journal of Water Resources Planning and Management, 135 (6). pp. 553-557. ISSN 0733-9496

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Abstract

After several decades in service, the deterioration of storm-water pipe assets is inevitable. The deterioration of storm-water pipes is characterized by structural deterioration and hydraulic deterioration. Condition assessment using closed circuit television (CCTV) inspection is often carried out to assess the structural condition of pipes. However, the knowledge on the condition of storm-water pipe assets is still limited for strategic planning of maintenance and rehabilitation, because generally only a small sample is CCTV-inspected and in almost all cases, these pipes are inspected once only due to high costs. The challenge for researchers is to use the sample of CCTV-inspected pipes for developing mathematical models that can predict the structural condition of remaining pipes as well as the future condition of pipes. In this present study, the deterioration pattern of storm-water pipes is constructed on the basis that each pipe has its own deterioration rate due to its pipe factors. Based on this, two mathematical models using multiple logistic regression �MLR� and probabilistic neural networks �PNN� are developed for predicting the structural condition of individual pipes. The MLR model was calibrated using the maximum likelihood method and the PNN model was trained using a genetic algorithm �GA�. The predictive performances of both models were compared using CCTV data collected for a local government authority in Melbourne, Australia. The results showed that the PNN model was more suited for modeling the structural deterioration of individual storm-water pipes than the MLR model. Furthermore, the use of GA improved the training results of the PNN model compared to the trial and error method.

Item Type: Article
Uncontrolled Keywords: ResPubID17429, storm-water management, regression models, probability, neural networks, structural failures, deterioration
Subjects: Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0905 Civil Engineering
SEO Classification > 9004 Water and Waste Services
Depositing User: VUIR
Date Deposited: 04 Oct 2011 03:55
Last Modified: 01 Mar 2015 22:45
URI: http://vuir.vu.edu.au/id/eprint/4641
DOI: 10.1061/(ASCE)0733-9496(2009)135:6(553)
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Citations in Scopus: 3 - View on Scopus

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