Pipe break prediction based on evolutionary data-driven methods with brief recorded data

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@Article{Xu2011942,
  author =       "Qiang Xu and Qiuwen Chen and Weifeng Li and 
                 Jinfeng Ma",
  title =        "Pipe break prediction based on evolutionary
                 data-driven methods with brief recorded data",
  journal =      "Reliability Engineering \& System Safety",
  volume =       "96",
  number =       "8",
  pages =        "942--948",
  year =         "2011",
  month =        aug,
  ISSN =         "0951-8320",
  DOI =          "doi:10.1016/j.ress.2011.03.010",
  URL =          "http://www.sciencedirect.com/science/article/B6V4T-52BWVVF-4/2/2cc722b50b1a73f1b86f4ef8e44660d4",
  keywords =     "genetic algorithms, genetic programming, Pipe break
                 model, Data-driven technique, Evolutionary polynomial
                 regression",
  abstract =     "Pipe breaks often occur in water distribution
                 networks, imposing great pressure on utility managers
                 to secure stable water supply. However, pipe breaks are
                 hard to detect by the conventional method. It is
                 therefore necessary to develop reliable and robust pipe
                 break models to assess the pipe's probability to fail
                 and then to optimise the pipe break detection scheme.
                 In the absence of deterministic physical models for
                 pipe break, data-driven techniques provide a promising
                 approach to investigate the principles underlying pipe
                 break. In this paper, two data-driven techniques,
                 namely Genetic Programming (GP) and Evolutionary
                 Polynomial Regression (EPR) are applied to develop pipe
                 break models for the water distribution system of
                 Beijing City. The comparison with the recorded pipe
                 break data from 1987 to 2005 showed that the models
                 have great capability to obtain reliable predictions.
                 The models can be used to prioritise pipes for break
                 inspection and then improve detection efficiency.",
}

Genetic Programming entries for Qiang Xu Qiuwen Chen Weifeng Li Jinfeng Ma

Citations