Aiming for the Holy Grail: Pipe Condition Assessment Along Critical Mains from Limited Inspections

Valls Miro, J; Shi, L

Aiming for the Holy Grail: Pipe Condition Assessment Along Critical Mains from Limited Inspections

Valls Miro, J

Shi, L

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Publisher:: Monkey Media
Publication Type:: Journal Article
Citation:: Utility Magazine, 2016, 10 pp. 90 - 92
Issue Date:: 2016-05-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Valls Miro, J https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Shi, L	en_US
dc.date.issued	2016-05-01	en_US
dc.identifier.citation	Utility Magazine, 2016, 10 pp. 90 - 92	en_US
dc.identifier.issn	2203-2797	en_US
dc.identifier.uri	http://hdl.handle.net/10453/87354
dc.description.abstract	The Advanced Condition Assessment and Pipe Failure Prediction Project is coming up with a novel condition assessment research concept: exploiting data-driven research to improve large critical water mains condition prediction, over extended sections of pipeline, from limited condition assessment inspection data.	en_US
dc.publisher	Monkey Media	en_US
dc.relation.ispartof	Utility Magazine	en_US
dc.title	Aiming for the Holy Grail: Pipe Condition Assessment Along Critical Mains from Limited Inspections	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.for	091303 Autonomous Vehicles	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.volume	10	en_US

Abstract:

The Advanced Condition Assessment and Pipe Failure Prediction Project is coming up with a novel condition assessment research concept: exploiting data-driven research to improve large critical water mains condition prediction, over extended sections of pipeline, from limited condition assessment inspection data.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/87354