World Environmental and Water Resources Congress 2020
Artificial Neural Networks and Adaptive Neuro-Fuzzy Models to Predict Remaining Useful Life of Water Pipelines
Publication: World Environmental and Water Resources Congress 2020: Water, Wastewater, and Stormwater and Water Desalination and Reuse
ABSTRACT
The U.S. water distribution system contains thousands of miles of pipes constructed from different materials, and of various sizes, and age. These pipes suffer from physical, environmental, structural, and operational stresses, causing deterioration which eventually leads to their failure. Pipe deterioration results in increased break rates, reduced hydraulic capacity, and detrimental impacts on water quality. Therefore, it is crucial to use accurate models to forecast deterioration rates along with estimating the remaining useful life of the pipes to implement essential interference plans to prevent catastrophic failures. This paper discusses a computational model that forecasts the RUL of water pipes by applying artificial neural networks (ANNs) as well as the adaptive neural fuzzy inference system (ANFIS). These models are trained and tested acquired field data to identify the significant parameters that impact the prediction of RUL. It is concluded that, on average, with approximately 10% of wall thickness loss in existing cast iron, ductile iron, asbestos-cement, and steel water pipes, the reduction of the remaining useful life is approximately 50%.
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Published In
World Environmental and Water Resources Congress 2020: Water, Wastewater, and Stormwater and Water Desalination and Reuse
Pages: 191 - 204
Editors: Sajjad Ahmad, Ph.D., and Regan Murray, Ph.D.
ISBN (Online): 978-0-7844-8298-8
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: May 14, 2020
Published in print: May 14, 2020
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