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    Development of Predictability and Condition Assessability Indices for PCCP Water Mains

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    Kola_R_T_2010.pdf (4.663Mb)
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    Date
    2010-01-25
    Author
    Kola, Rajyalakshmi
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    Abstract
    The condition of water and wastewater pipelines has been deteriorating with time and since this infrastructure is out-of-sight, the assessment has been neglected over the years. The advancement of technology in various fields has provided pathway for development of several technologies for assessment of the condition of pipeline systems. However, there is no standard guidance or tool for the utilities to use these technologies appropriately. The utilities are unaware of the present state-of- the- art technologies. The predictability and condition assessability indices will help utilities predict a probable failure and take steps to prevent it. The predictability index will indicate the inherent, theoretical predictability of key types of pipe failures. The pipe failure predictability index would be a score calculated by identifying high priority pipe types, characterizing their failure modes, mechanism, conditions, and indicators, reliability of indicators, lead-time of the indicators, and other factors. The condition assessability index will indicate the technical and economical methods of preventing key types of pipe failures. The pipe failure condition assessability index is similar to the predictability index, but it takes into account the capability of existing inspection technologies for measuring the required failure indicator parameters. Prestressed Concrete Cylinder Pipes are used in large diameter water pipelines throughout the United States to convey large volumes of water. Prestressed Concrete Cylinder Pipes are complex composite pipes. Therefore, prediction and prevention of failure of these pipelines is complex and requires a better understanding of the system. This research concentrates around development of Predictability and Condition Assessability Indices for PCCP pipelines.
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    http://hdl.handle.net/10919/31181
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    • Masters Theses [20805]

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