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The U.S. water distribution system contains thousands of miles of pipesconstructed from different materials, and of various sizes, and age. Thesepipes suffer from physical, environmental, structural and operational stresses,causing deterioration which eventually leads to their failure. Pipedeterioration results in increased break rates, reduced hydraulic capacity, anddetrimental impacts on water quality. Therefore, it is crucial to use accuratemodels to forecast deterioration rates along with estimating the remaininguseful life of the pipes to implement essential interference plans in order toprevent catastrophic failures. This paper discusses a computational model thatforecasts the RUL of water pipes by applying Artificial Neural Networks (ANNs)as well as Adaptive Neural Fuzzy Inference System (ANFIS). These models aretrained and tested acquired field data to identify the significant parametersthat impact the prediction of RUL. It is concluded that, on average, withapproximately 10\% of wall thickness loss in existing cast iron, ductile iron,asbestos-cement, and steel water pipes, the reduction of the remaining usefullife is approximately 50%

Artificial Neural Networks and Adaptive Neuro-fuzzy Models for Prediction of Remaining Useful Life