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首页> 外文期刊>Journal of Hydroinformatics >Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling
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Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling

机译：使用数据驱动的模型评估配水管的管道故障率和机械可靠性

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In this paper two models are presented based on Data-Driven Modeling (DDM) techniquesn(Artificial Neural Network and neuro-fuzzy systems) for more comprehensive and more accuratenprediction of the pipe failure rate and an improved assessment of the reliability of pipes.nFurthermore, a multivariate regression approach has been developed to enable comparison withnthe DDM-based methods. Unlike the existing simple regression models for prediction of pipenfailure rates in which only few factors of diameter, age and length of pipes are considered, in thisnpaper other parameters such as pressure and pipe depth, are also included. Furthermore, anninvestigation is carried out on most commonly used mechanical reliability relationships and thenresults of incorporation of the proposed pipe failure models in the reliability index are compared.nThe proposed models are applied to a real case study involving a large water distribution networknin Iran and the results of model predictions are compared with measured pipe failure data.nCompared with the results of neuro-fuzzy and multivariate regression models, the outcomes ofnthe artificial neural network model are more realistic and accurate in the prediction of pipe failurenrates and evaluation of mechanical reliability in water distribution networks.
机译：本文基于数据驱动建模（DDM）技术n（人工神经网络和神经模糊系统）提出了两个模型，用于更全面，更准确地预测管道故障率并改进对管道可靠性的评估。已经开发出多元回归方法以与基于DDM的方法进行比较。与现有的简单预测管道失效率的简单回归模型不同，在该模型中仅考虑了管道直径，寿命和长度的几个因素，因此本文还包括其他参数，例如压力和管道深度。此外，对最常用的机械可靠性关系进行了调查，然后比较了将建议的管道破坏模型纳入可靠性指标的结果。n将建议的模型应用于涉及伊朗大型供水网络的实际案例研究，并得出了结果模型预测与测量的管道故障数据进行比较。n与神经模糊和多元回归模型的结果相比，人工神经网络模型的结果在预测管道故障率和评估配水机械可靠性方面更加真实，准确。网络。

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