Leak Detection and Topology Identification in Pipelines Using Fluid Transients and Artificial Neural Networks-Reference-Cited by-同舟云学术

Leak Detection and Topology Identification in Pipelines Using Fluid Transients and Artificial Neural Networks

Published:2020-06 Issue:6 Volume:146 Page:04020040
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Bohorquez Jessica¹^ORCID,Alexander Bradley²,Simpson Angus R.³,Lambert Martin F.⁴

Affiliation:

1. Ph.D. Candidate, School of Civil, Environmental, and Mining Engineering, Univ. of Adelaide, Adelaide, SA 5005, Australia. ORCID: .

2. Senior Lecturer, School of Computer Science, Univ. of Adelaide, Adelaide, SA 5005, Australia.

3. Professor, School of Civil, Environmental, and Mining Engineering, Univ. of Adelaide, Adelaide, SA 5005, Australia (corresponding author).

4. Professor, School of Civil, Environmental, and Mining Engineering, Univ. of Adelaide, Adelaide, SA 5005, Australia.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001187

Reference56 articles.

1. Methods of Assessment of Water Losses in Water Supply Systems: a Review

2. Leak detection in liquefied gas pipelines by artificial neural networks

3. Bohorquez J. A. R. Simpson and M. F. Lambert. 2018. “Characterization of transient pressure traces due to the effects of different anomalies and features in water pipelines.” In Proc. 13th Pressure Surges Conf. 151–169. Cranfield UK: BHR Group.

4. Transient Test-Based Technique for Leak Detection in Outfall Pipes

5. Portable pressure wave-maker for leak detection and pipe system characterization

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