Improvement of fracture assessment method for pipe girth weld based on failure assessment diagram

Wu, Kai; Zhang, Dong; Feng, Qingshan; Yang, Yue; Dai, Lianshuang; Wang, Dongying; Zhang, Hong; Guo, GuangFei; Liu, Xiaoben

doi:10.1016/j.ijpvp.2023.104950

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Many factors affect the failure risk of girth welds in oil and gas transmission pipelines, but the industry generally agrees that the three key factors are the pipe materials and properties, weld defects, and loads [26,30]. Based on the samples collected, the factors that cause girth weld failures can be divided into three categories: pipe type and performancerelated indicators, defect-related indicators, and load-related indicators.…”

Section: Influencing Factors and Normalizationmentioning

confidence: 99%

“…For girth welds, Chang [25] used a numerical simulation method based on the Gurson-Tvergaard-Needleman (GTN) model to analyze the crack initiation and dynamic fracture behavior of a welded pipe under pure bending load. Wu et al [26]. investigated the effects of the crack size, pipe diameter-thickness ratio, and material parameters on the fracture assessment accuracy of pipe girth welds based on the failure assessment diagram theory and the equivalent stress-strain relationship method.…”

Section: Introductionmentioning

confidence: 99%

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Failure Risk Prediction Model for Girth Welds in High-Strength Steel Pipeline Based on Historical Data and Artificial Neural Network

Wang

Zhang

Guo³

et al. 2023

Processes

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Pipelines are the most economical and sensible way to transport oil and gas. Long-distance oil and gas pipelines consist of many steel pipes or pipe fittings joined by welded girth welds, so girth welds are an essential part of pipelines. Owing to the limitations of welding conditions and the complexity of controlling weld quality in the field, some defects are inevitably present in girth welds and adjacent weld areas. These defects can lead to pipeline safety problems; therefore, it is necessary to perform failure risk assessment of pipeline girth welds. In this study, an artificial neural network model was proposed to predict the failure risk of pipeline girth welds with defects. Firstly, many pipeline girth weld failure cases, pipeline excavation, and inspection data were collected and analyzed to determine the main factors influencing girth weld failure. Secondly, a spatial orthogonal optimization method was used to select training samples for the artificial neural network model to ensure that the training sample set could cover the feature space with a minimum number of samples. Thirdly, a prediction model based on BP neural networks was established to predict the failure risk levels. The training dataset/testing dataset was 602/4215, and the prediction accuracy for all risks of girth welds achieved an acceptable level. This study can provide a valuable reference for pipeline operators to prevent pipeline accidents.

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Section: Influencing Factors and Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%