Abstract The integrity of pipelines hinges on the strain capacity of girth welds, making it a pivotal factor in ensuring pipeline safety. The large-scale adoption of large-diameter X80 pipelines and the application of fully automatic welding technology have led to significant differences between the I-II composite fracture failure mechanism in fully automatic welded composite bevel girth welds and the traditional I-type fracture failure mechanism observed in manual welding or combination automatic welding with a single V-shaped bevel. Through statistical analysis of construction-phase data from a typical large-diameter X80 pipeline, this study investigates the probabilistic distribution patterns of uncertainties affecting girth weld failures. It identifies the optimal distribution types for uncertain parameters such as material mechanical properties, loads, and defects. By applying the Hamiltonian Monte Carlo-Subset Simulation algorithm, a reliability analysis was conducted on the strain capacity prediction model for large-diameter X80 fully automatic welded composite bevel girth welds, known as the CUP model. This research systematically explores the influence of material parameters (base metal yield-to-tensile strength ratio, weld strength matching coefficient), defect parameters (crack depth, crack length, misalignment), and load parameters (strain capacity demand) on the reliability of high-grade steel pipeline girth welds with cracks. The results indicate that by varying the mean values and standard deviation of the parameters involved in the reliability analysis and examining their effects on girth weld reliability, it was found that the reliability of girth welds with cracks decreases with increasing crack depth, crack length, and base metal yield-to-tensile strength ratio, but increases with a higher strength-matching coefficient of the weld material. The findings of this research provide valuable insights, offering a reference and guidance for the reliability-based design of girth welds in high-grade steel pipelines.
Zhang et al. (Sun,) studied this question.
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