ABSTRACT

The water distribution system in the United States consists of 2.2 million miles of pipelines with water main breaks that the American Society of Civil Engineers (ASCE) 2021 Report Card estimated results in six billion gallons of drinking water lost daily. Detecting the state of pipeline deterioration should always be a top priority in the water pipeline industry. This paper statistically evaluates data from more than 70,000 electromagnetic inspections that have been performed since the installation of these pipes in 1971. The scope of this paper is related to performance and degradation of prestressed concrete cylinder pipe (PCCP) which is used in large diameter water transmission to predict remaining useful life (RUL) using different modeling technologies such as the artificial neural network. The objective of this research is to prepare a model to predict the RUL of PCCP. The results showed that the artificial neural network (ANN) method is very accurate method for calculating the (RUL) with a 97.9% accuracy value. Broken wires and internal water pressure were found to be the most important parameters to calculate the RUL of PCCP for the two diameters 72- and 90-in. considered. This paper should serve as a guideline for future research designed to investigate additional diameters.

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REFERENCES

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Go to Pipelines 2023
Pipelines 2023
Pages: 286 - 295

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Published online: Aug 10, 2023

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Ahmad Jibreen, Ph.D. [email protected]
1Adjunct Professor and Researcher, Dept. of Civil Engineering, Center for Underground Infrastructure Research and Education, Univ. of Texas at Arlington, Arlington, TX. Email: [email protected]
Mohammad Najafi, Ph.D., F.ASCE [email protected]
P.E.
2Professor and Director, Dept. of Civil Engineering, Center for Underground Infrastructure Research and Education, Univ. of Texas at Arlington, Arlington, TX. Email: [email protected]
Vinayak Kaushal, Ph.D., A.M.ASCE [email protected]
3Assistant Professor of Instruction and Researcher, Dept. of Civil Engineering, Center for Underground Infrastructure Research and Education, Univ. of Texas at Arlington, Arlington, TX. Email: [email protected]
Kawalpreet Kaur [email protected]
4Ph.D. Student, Dept. of Civil Engineering, Center for Underground Infrastructure Research and Education, Univ. of Texas at Arlington, Arlington, TX. Email: [email protected]
Madhuri Arjun [email protected]
5Ph.D. Student, Dept. of Civil Engineering, Center for Underground Infrastructure Research and Education, Univ. of Texas at Arlington, Arlington, TX. Email: [email protected]
Salar Shirkhanloo, Ph.D. [email protected]
6Clinical Assistant Professor, Univ. of North Texas, Denton, TX. Email: [email protected]

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