Comparison of Structural Deterioration Models for Stormwater Drainage Pipes

Full text for this resource is not available from the Research Repository.

Tran, D. H, Perera, B. J. C and Ng, A. W. M (2009) Comparison of Structural Deterioration Models for Stormwater Drainage Pipes. Computer-Aided Civil and Infrastructure Engineering , 24 (2). pp. 145-156. ISSN 1093-9687


Structural deterioration of pipes is the continuing reduction of load bearing capacity, which can be characterized through structural defects. Structural deterioration has been a major concern for asset managers in maintaining the required performance of stormwater drainage systems in Australia. Condition assessment using closed circuit television (CCTV) inspection is often carried out to assess the deteriorating condition of individual pipes. In this study, two models were developed using ordered probit and neural networks (NNs) techniques for predicting the structural condition of individual pipes. The predictive performances were compared using CCTV data collected for a local government authority in Melbourne, Australia. The significant input factors to the outputs of both models were also identified. The results showed that the NN model was more suitable for modeling structural deterioration than the ordered probit model. The hydraulic condition, pipe size, and pipe location were found to be significant factors for this case study.

Dimensions Badge

Altmetric Badge

Item type Article
DOI 10.1111/j.1467-8667.2008.00577.x
Official URL
Subjects Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > FOR Classification > 0905 Civil Engineering
Historical > SEO Classification > 9004 Water and Waste Services
Keywords ResPubID17426, structural deterioration of pipes, structural defects, performance of stormwater drainage systems – Australia, condition assessments, probit and neural networks (NNs) techniques, predicting structural condition
Citations in Scopus 37 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login