Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models-Reference-Cited by-同舟云学术

Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models

Published:2016-10 Issue:5 Volume:30 Page:04016032
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Zangenehmadar Zahra¹,Moselhi Osama²

Affiliation:

1. Ph.D. Candidate, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., Montreal, QC, Canada H3G 1M8 (corresponding author).

2. Dept. of Building, Civil and Environmental Engineering, Concordia Univ., Montreal, QC, Canada H3G 1M8.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CF.1943-5509.0000886

Reference29 articles.

1. Prediction of Water Pipe Asset Life Using Neural Networks

2. Condition Rating Model for Underground Infrastructure Sustainable Water Mains

3. Amaitik N. M. and Amaitik S. M. (2008). “Development of PCCP wire breaks prediction model using artificial neural networks.” Proc. Int. Pipelines Conf. ASCE Atlanta 1–11.

4. ASCE. (2013). Report card for America’s infrastructure 〈www.infrastructurereportcard.org〉 (Sep. 11 2015).

5. National guide to sustainable municipal infrastructure;Boudreau S.;Professional Engineers and Geoscientist of BC-Newsletter of the Municipal Engineers Division,2003

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