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.
(Some files may be inaccessible until you login with your Deakin Research Online credentials)
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.
(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
Field of Research
120199 Architecture not elsewhere classified
Socio Economic Objective
970112 Expanding Knowledge in Built Environment and Design
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact email@example.com.