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    Future Inspection and Deterioration Prediction Capabilities for Buried Distributed Water Infrastructure

    Source: Journal of Pipeline Systems Engineering and Practice:;2022:;Volume ( 013 ):;issue: 003::page 04022020
    Author:
    Lewis O. Makana
    ,
    Will J. Shepherd
    ,
    Simon Tait
    ,
    Christopher D. F. Rogers
    ,
    Nicole Metje
    ,
    Joby B. Boxall
    ,
    Alma N. A. Schellart
    DOI: 10.1061/(ASCE)PS.1949-1204.0000656
    Publisher: ASCE
    Abstract: This paper examines the role of pipe deterioration prediction approaches for optimizing maintenance, repair, and rehabilitation of buried water supply, wastewater collection, and drainage networks. It is appreciated that there are other ancillary assets within water supply and wastewater collection and drainage networks, but these were not considered in this paper. Currently there are a range of asset condition assessment frameworks, mainly based on asset defect location, identification, and characterization. These are infrequently applied in practice, mainly due to the restricted availability of asset defect inspection data. This paper reviews current deterioration modeling approaches and highlights the crucial need for broader, richer data sets (including both asset and surrounding environment data) to inform the development and application of such approaches. This paper describes what could be considered as an expanded ideal data set for deterioration modeling at a network and individual asset scale and indicates emerging new inspection technologies that should be capable of meeting the enhanced data needs.
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      Future Inspection and Deterioration Prediction Capabilities for Buried Distributed Water Infrastructure

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4286624
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    contributor authorLewis O. Makana
    contributor authorWill J. Shepherd
    contributor authorSimon Tait
    contributor authorChristopher D. F. Rogers
    contributor authorNicole Metje
    contributor authorJoby B. Boxall
    contributor authorAlma N. A. Schellart
    date accessioned2022-08-18T12:26:16Z
    date available2022-08-18T12:26:16Z
    date issued2022/05/17
    identifier other%28ASCE%29PS.1949-1204.0000656.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4286624
    description abstractThis paper examines the role of pipe deterioration prediction approaches for optimizing maintenance, repair, and rehabilitation of buried water supply, wastewater collection, and drainage networks. It is appreciated that there are other ancillary assets within water supply and wastewater collection and drainage networks, but these were not considered in this paper. Currently there are a range of asset condition assessment frameworks, mainly based on asset defect location, identification, and characterization. These are infrequently applied in practice, mainly due to the restricted availability of asset defect inspection data. This paper reviews current deterioration modeling approaches and highlights the crucial need for broader, richer data sets (including both asset and surrounding environment data) to inform the development and application of such approaches. This paper describes what could be considered as an expanded ideal data set for deterioration modeling at a network and individual asset scale and indicates emerging new inspection technologies that should be capable of meeting the enhanced data needs.
    publisherASCE
    titleFuture Inspection and Deterioration Prediction Capabilities for Buried Distributed Water Infrastructure
    typeJournal Article
    journal volume13
    journal issue3
    journal titleJournal of Pipeline Systems Engineering and Practice
    identifier doi10.1061/(ASCE)PS.1949-1204.0000656
    journal fristpage04022020
    journal lastpage04022020-14
    page14
    treeJournal of Pipeline Systems Engineering and Practice:;2022:;Volume ( 013 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian