Onshore oil and gas pipelines are often buried beneath the river bed and channel banks. One of the primary reasons for the exposure of buried pipelines is the scouring mechanism that occurs when shear stress induced on riverbed by flowing water exceeds the resistance of channel bed material. Depending on the free spanning length and watercourse flow velocity, the vortex shedding phenomena may cause interactions resulting in a catastrophic pipeline failure. Accurate estimation of parameters that influence critical span length and scour depth become extremely important to maintain the integrity of the pipeline system and optimize its effective service life. This study is aimed at quantifying the relative importance of input variables used in predicting critical span length and scour depth based on the weights obtained from an Artificial Neural Network (ANN). The Artificial Neural Network model is developed by collecting pipeline accident reports from Pipeline and Hazardous Material Safety Administration (PHMSA) database for accidents that occurred due to Vortex Induced Vibration (VIV) loading during flooding in the last 35 years. It is seen that factors such as internal fluid pressure, dynamic lateral and vertical soil stiffness, reduced velocity and age of pipeline have a significant contribution in terms of model weights and help in accurately assessing the pipeline’s vulnerability to failure.

中文翻译：

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使用ANN定量预测陆上跨河管道临界跨度和冲刷深度的变量重要性

陆上石油和天然气管道通常被埋在河床和河床下方。埋入地下管道暴露的主要原因之一是冲刷机理，该冲刷机理是当流水在河床上引起的切应力超过通道床材料的阻力时发生的。取决于自由跨度和河道流速，涡旋脱落现象可能引起相互作用，导致灾难性的管道故障。准确估计影响临界跨度和冲刷深度的参数对于维持管道系统的完整性和优化其有效使用寿命至关重要。这项研究旨在根据从人工神经网络（ANN）获得的权重量化用于预测临界跨度和冲刷深度的输入变量的相对重要性。人工神经网络模型是通过从管道和有害物质安全管理局（PHMSA）数据库收集管道事故报告而开发的，该报告是针对过去35年中在洪水期间由于涡激振动（VIV）负荷而发生的事故。可以看出，诸如内部流体压力，动态横向和垂直土壤刚度，降低的管道速度和使用期限等因素在模型权重方面具有重要作用，并有助于准确评估管道的故障脆弱性。人工神经网络模型是通过从管道和有害物质安全管理局（PHMSA）数据库收集管道事故报告而开发的，该报告是针对过去35年中在洪水期间由于涡激振动（VIV）负荷而发生的事故。可以看出，诸如内部流体压力，动态横向和垂直土壤刚度，降低的管道速度和使用期限等因素在模型权重方面具有重要作用，并有助于准确评估管道的故障脆弱性。人工神经网络模型是通过从管道和有害物质安全管理局（PHMSA）数据库收集管道事故报告而开发的，该报告是针对过去35年中在洪水期间由于涡激振动（VIV）负荷而发生的事故。可以看出，诸如内部流体压力，动态横向和垂直土壤刚度，降低的管道速度和使用期限等因素在模型权重方面具有重要作用，并有助于准确评估管道的故障脆弱性。