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

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