Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Project deliverable . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Project deliverable . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2022
License: CC BY
Data sources: ZENODO
versions View all 2 versions
addClaim

D7.1. Report on methodologies used to identify in-pipe defects

Authors: Tait, Simon; Kazemi, Ehsan;

D7.1. Report on methodologies used to identify in-pipe defects

Abstract

This report is Deliverable 7.1 “Report on the methodologies used to identify in-pipe defects” of the Co- UDlabs project, which is funded under the European Union’s Horizon 2020 research and innovation programme via Grant Agreement No 101008626. The Deliverable is an output from Work Package 7, “Asset Deterioration”. The University of Sheffield (UFSD). UFSD is the author of this deliverable. The report describes current defect inspection and classification methodologies and provides a review of their effectiveness. The report argues that historically there has been a strong linkage between the development of international and national defect classification systems and sewer inspection approaches. A state-of-the-art review of commonly used inspection technologies is provided. The potential for newer emerging inspection technologies is also described and discussed. These new approaches are being developed to (i) reduce cost so as to allow the wider use of sewer inspection and (ii) gather more physically relevant information about in-sewer defects so that more physically based pipe deterioration models can be developed, and (iii) to provide inspection data with less uncertainty. If improvements in inspection approaches could be achieved it would allow water utilities to better focus investment for sewer rehabilitation and renewal and also more rapidly identify operational issues that cause system failures such as flooding and the release of untreated wastewater via sewer overflows. Different sources of information were considered: (i) a search of academic and governmental agency sources – peer reviewed outputs; (ii) search of documentation from other sources that had not been reviewed; and (iii) information from commercial sources, mainly companies providing in-pipe inspection services. This review has shown that CCTV based inspection currently still dominates sewer inspection, despite the recognition that there is significant uncertainty and cost associated with human based analysis of CCTV images. Different technologies have been developed and deployed to identify defects that are difficult to identify using visual means and that also allow for larger proportions of sewer networks to be inspected. The review has also shown the benefits of multi-sensor approaches when identifying and characterising sewer pipe defects. Emerging inspection technologies can be organised into 3 groups: new sensing technologies, autonomous, multi-sensor inspection platform and adaptation of AI based approaches to better identify and characterise in-pipe defects from CCTV images. It is also clear that new inspection techniques are being developed so that more physically relevant inspection data can be collected to inform the development and use of more physically based pipe/asset structural deterioration models.

Related Organizations
Keywords

Joint Research Activity, Co-UDlabs, Research Infrastructures, Urban Drainage Systems

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 9
    download downloads 12
  • 9
    views
    12
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
9
12
Green