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Application of neural networks in modelling serviceability deterioration of concrete stormwater pipes

Ng, A. W. M., Tran, D. H., Osman, N. Y. and McManus, K. J. 2006, Application of neural networks in modelling serviceability deterioration of concrete stormwater pipes, in NN'06 : Proceedings of the 7th WSEAS International Conference on Neural Networks, WSEAS, [Cavtat, Croatia], pp. 53-60.

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Title Application of neural networks in modelling serviceability deterioration of concrete stormwater pipes
Author(s) Ng, A. W. M.
Tran, D. H.
Osman, N. Y.
McManus, K. J.
Conference name Proceedings of the 7th WSEAS International Conference on Neural Networks (2006 : Cavtat, Croatia)
Conference location Cavtat, Croatia
Conference dates 12 - 15 Jun. 2006
Title of proceedings NN'06 : Proceedings of the 7th WSEAS International Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2006
Conference series International Conference on Neural Networks
Start page 53
End page 60
Publisher WSEAS
Place of publication [Cavtat, Croatia]
Keyword(s) deterioration model
neural networks
stormwater pipes
multiple discriminant analysis
Summary Stormwater pipe systems in Australia are designed to convey water from rainfall and surface runoff only and do not transport sewage. Any blockage can cause flooding events with the probability of subsequent property damage. Proactive maintenance plans that can enhance their serviceability need to be developed based on a sound deterioration model. This paper uses a neural network (NN) approach to model deterioration in serviceability of concrete stormwater pipes, which make up the bulk of the stormwater network in Australia. System condition data was collected using CCTV images. The outcomes of model are the identification of the significant factors influencing the serviceability deterioration and the forecasting of the change of serviceability condition over time for individual pipes based on the pipe attributes. The proposed method is validated and compared with multiple discriminant analysis, a traditionally statistical method. The results show that the NN model can be applied to forecasting serviceability deterioration. However, further improvements in data collection and condition grading schemes should be carried out to increase the prediction accuracy of the NN model.
Notes (Won Best Paper Award based on best originality and scientific impact, good presentation (presented by Osman, N.Y) and paper presented by student) Reproduced with the kind permission of the copyright owner
Language eng
Field of Research 120199 Architecture not elsewhere classified
Socio Economic Objective 970112 Expanding Knowledge in Built Environment and Design
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2006, WSEAS
Persistent URL http://hdl.handle.net/10536/DRO/DU:30031956

Document type: Conference Paper
Collections: School of Architecture and Built Environment
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