Fachinformationsdienst BAUdigital

A platform for research: civil engineering, architecture and urbanism

    Predicting Pipe Breaks: Desktop Scoring, Advanced Statistics (LEYP), and Machine Learning

    Using desktop scoring to determine the likelihood of failure of water pipes should be phased out and replaced with advanced analytics, particularly machine learning.

    More accurate break predictions will lead to better estimates of how much to spend on pipe replacement and which pipes to replace.

    Utilities should integrate information about abandoned pipes and breaks into failure forecasting, including machine learning models.

    Some data issues can be cost‐efficiently rectified using machine learning and automated algorithms.

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Page navigation

    Document information
    Similar titles

    Export, share and cite

    Predicting Pipe Breaks: Desktop Scoring, Advanced Statistics (LEYP), and Machine Learning

    Using desktop scoring to determine the likelihood of failure of water pipes should be phased out and replaced with advanced analytics, particularly machine learning.

    More accurate break predictions will lead to better estimates of how much to spend on pipe replacement and which pipes to replace.

    Utilities should integrate information about abandoned pipes and breaks into failure forecasting, including machine learning models.

    Some data issues can be cost‐efficiently rectified using machine learning and automated algorithms.

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Predicting Pipe Breaks: Desktop Scoring, Advanced Statistics (LEYP), and Machine Learning

    Using desktop scoring to determine the likelihood of failure of water pipes should be phased out and replaced with advanced analytics, particularly machine learning.

    More accurate break predictions will lead to better estimates of how much to spend on pipe replacement and which pipes to replace.

    Utilities should integrate information about abandoned pipes and breaks into failure forecasting, including machine learning models.

    Some data issues can be cost‐efficiently rectified using machine learning and automated algorithms.

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Page navigation

    Document information
    Similar titles

    Export, share and cite

    Document information
    Title:

    Predicting Pipe Breaks: Desktop Scoring, Advanced Statistics (LEYP), and Machine Learning

    Contributors:

    Raven, Annie Vanrenterghem (author) / Campanella, Kevin V. (author)

    Published in:

    Journal ‐ American Water Works Association ; 115 ; 42-53

    Publication date:

    2023-05-01

    Size:

    1 pages

    ISSN:

    0003-150X , 1551-8833

    DOI:

    https://doi.org/10.1002/awwa.2089

    Type of media:

    Article (Journal)

    Type of material:

    Electronic Resource

    Language:

    English

    Keywords:

    Pipes , Rehabilitation , Statistics , Utilities , Forecasting , Modeling , Costs

    Similar titles

    Predicting water pipe breaks using neural network

    Ahn, J.C. | Online Contents | 2005

    Predicting water pipe breaks using neural network

    Ahn, J. C. / Lee, S. W. / Lee, G. S. et al. | British Library Conference Proceedings | 2005

    Detecting breaks in prestressing pipe wire

    Atherton, David L. / Morton, Keith / Mergelas, Brian J. | Wiley | 2000

    Breaks in Cast Iron Pipe Gridiron Systems

    Mabee, W. C. | Wiley | 1932

    Modeling Water Pipe Breaks-Three Case Studies

    Pelletier, G. / Mailhot, A. / Villeneuve, J.-P. | British Library Online Contents | 2003

    Back to top