Prediction of Water Pipe Asset Life Using Neural Networks

This paper describes investigations into a development of a new application of neural networks (NN) for prediction of pipeline failure. Results show higher correlations with recorded data when compared with the two existing statistical models. The shifted time power model gives results in total number of failures and the shifted time exponential model gives results in number of failures per year. The database was large but neither complete and nor fully accurate. Factors influencing pipeline deterioration were missing from the database. Using the NN technique on this database produced models of pipeline failure, in terms of failures/km/year, that more closely matched the number of failures of a particular asset recorded for the period.