外文翻译------一个预测埋地pvc管道故障率的物理模型.doc

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外文翻译------一个预测埋地pvc管道故障率的物理模型,reliability engineering and system safety 92 (2007) 1258¨c1266a physical probabilistic model to predict failure rates in buried pvc pipelinesp. davis_, s. burn,...
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Reliability Engineering and System Safety 92 (2007) 1258¨C1266
A physical probabilistic model to predict failure rates in buried PVC pipelines
P. Davis_, S. Burn, M. Moglia, S. Gould
CSIRO Land and Water, Graham Road, Highett, Vic. 3190, Australia
Received 28 September 2005; received in revised form 28 July 2006; accepted 8 August 2006
Available online 29 September 2006
Abstract
For older water pipeline materials such as cast iron and asbestos cement, future pipe failure rates can be extrapolated from large volumes of existing historical failure data held by water utilities. However, for newer pipeline materials such as polyvinyl chloride (PVC), only limited failure data exists and confident forecasts of future pipe failures cannot be made from historical data alone. To solve this problem, this paper presents a physical probabilistic model, which has been developed to estimate failure rates in buried PVC pipelines as they age. The model assumes that under in-service operating conditions, crack initiation can occur from inherent defects located in the pipe wall. Linear elastic fracture mechanics theory is used to predict the time to brittle fracture for pipes with internal defects subjected to combined internal pressure and soil deflection loading together with through-wall residual stress. To include uncertainty in the failure process, inherent defect size is treated as a stochastic variable, and modelled with an appropriate probability distribution. Microscopic examination of fracture surfaces from field failures in Australian PVC pipes suggests that the 2-parameter Weibull distribution can be applied. Monte Carlo simulation is then used to estimate lifetime probability distributions for pipes with internal defects, subjected to typical operating conditions. As with inherent defect size, the 2-parameter Weibull distribution is shown

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