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    Decision Support Tool for Optimal Replacement of Plumbing Systems

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    Abstract.pdf (124.7Kb)
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    Chapter1.pdf (376.6Kb)
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    Chapter2.pdf (619.4Kb)
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    Chapter3.pdf (361.2Kb)
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    Chapter4.pdf (349.0Kb)
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    Chapter5.pdf (698.5Kb)
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    Chapter6.pdf (12.93Kb)
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    Chapter7.pdf (45.25Kb)
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    Glossary.pdf (30.46Kb)
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    Appendix_A.pdf (24.62Kb)
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    Appendix_B.pdf (437.6Kb)
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    Appendix_C.pdf (130.1Kb)
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    VITA.pdf (9.557Kb)
    Downloads: 19
    Date
    2004-12-10
    Author
    Lee, Juneseok
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    Abstract
    Pinhole corrosion leak in home plumbing has emerged as a significant issue. In the major water distribution system managed by municipalities and water utilities the costs are distributed among all subscribers. The home plumbing repair/replacement cost and possible water damage cost must be addressed by the home owner. There are also issues of the value of home, insurance rates, health consequences, and taste and odor problems. These issues have become major concerns to home owners. Cradle to grave life cycle assessment is becoming an integral part of industrial manufacturing. In this thesis comprehensive details pertaining to life cycle assessment are presented. Copper tubing for plumbing installations is mainly obtained from recycled copper. Various stages of copper plumbing pipe manufacturing are explained. A comprehensive synthesis of various corrosion mechanisms is presented. Particular reference is given to copper plumbing pipe corrosion. A decision support tool for replacing copper plumbing pipes is presented. The deterioration process is grouped into early, normal and late stages. Because available data reflects late stage process, an optimization, neural network and curve fitting models are developed to infer early and normal stage behavior of the plumbing system. Utilizing the inferred leak rates a non-homogeneous poisson process model is developed to generate leak arrival times. An economically sustainable replacement criterion is adopted to determine optimal replacement time.
    URI
    http://hdl.handle.net/10919/36421
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    • Masters Theses [20811]

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