Neural Networks Deterioration Models for Serviceability Condition of Buried Stormwater Pipes

Buried stormwater pipe networks play a key role in surface drainage systems for urban areas of Australia. The pipe networks are designed to convey water from rainfall and surface runoff only and do not transport sewage. The deterioration of stormwater pipes is commonly graded into structural and serviceability condition using CCTV inspection data in order to recognize two different deterioration processes and consequences. This study investigated the application of neural networks modelling (NNM) in predicting serviceability deterioration that is associated with reductions of pipe diameter until a complete blockage. The outcomes of the NNM are predictive serviceability condition for individual pipes, which is essential for planning proactive maintenance programs, and ranking of pipe factors that potentially contribute to the serviceability deterioration. In this study the Bayesian weight estimation using Markov Chain Monte Carlo simulation was used for calibrating the NNM on a case study in order to account for the uncertainty often encountered in NNM’s calibration using conventional back-propagation weight estimation. The performance and the ranked factors obtained from the NNM were also compared against a classical model using multiple discrimination analysis (MDA). The results showed that the predictive performance of the NNM using Bayesian weight estimation is better than that of the NNM using conventional backpropagation and MDA model. Furthermore, among nine input factors, ‘pipe age’ and ‘location’ appeared insignificant whilst ‘pipe size’, ‘slope’, ‘the number of trees’ and ‘climatic condition’ were found consistently important over both models for serviceability deterioration process. The remaining three factors namely, ‘structure’, ‘soil’ and ‘buried depth’ might be redundant factors. A better and more consistent data collection regime may help to improve the predictive performance of the NNM and identify the significant factors