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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.

Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANN