Abstract
Abstract: 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.
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