Pressure data-driven model for failure prediction of PVC pipelines

Author:

Dawood Thikra,Elwakil Emad,Mayol Novoa Hector,Fernando Gárate Delgado José

Publisher

Elsevier BV

Subject

General Engineering,General Materials Science

Reference44 articles.

1. ASCE 2017. Infrastructure Report Card. https://www.infrastructurereportcard.org/cat-.

2. Snider, B., and McBean, E. A. 2020. Improving Urban Water Security through Pipe-Break Prediction Models: Machine Learning or Survival Analysis. Journal of Environmental Engineering, 146(3): 04019129.

3. Folkman, S. 2018. Water main break rates in the USA and Canada: a comprehensive study. https://digitalcommons.usu.edu/mae_facpub/174/.item/drinking_water/.

4. Predicting the timing of water main failure using artificial neural networks;Harvey;J. Water Resour. Plann. Manage.,2014

5. Buried No Longer: Confronting America’s Water Infrastructure Challenge.http://www.urbanwaterslearningnetwork.org/resources/awwa-confronting-americas-water-infrastructure-challenge2017/;AWWA,2012

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