Ultimate bearing capacity of X80 pipelines with incomplete penetration defects

承载力 渗透(战争) 结构工程 管道运输 材料科学 法律工程学 工程类 机械工程 运筹学
作者
Bin Jia,Youcai Xiang,Hongbing Zhang,Xinghong Zou,Li Zhu,Hongzhi Xiong
出处
期刊:Journal of Constructional Steel Research [Elsevier BV]
卷期号:235: 109830-109830 被引量:1
标识
DOI:10.1016/j.jcsr.2025.109830
摘要

Incomplete penetration defects pose a significant threat to the safe operation of pipelines. Therefore, this study conducted numerical simulations to investigate the influence of incomplete penetration defects on the mechanical properties and ultimate load-bearing capacity of a 1422 mm diameter X80 pipeline, focusing on the strain distribution in defective pipelines and evaluating the ultimate capacity for pipelines with various defect characteristics. The results indicated that under internal pressure, the maximum strain occurred at the four corners of the defect, while the strain concentration shifted to the bottom of the defect when an additional bending moment was applied. Furthermore, it was found that the maximum strain increased with the depth and length of the defect, but decreased as the pipe wall thickness and defect width increased. Based on the three factors that had a more significant impact on pipeline strain—pipe diameter, defect depth, and defect width—a neural network model and a predictive equation were created to estimate the ultimate bearing capacity of pipelines with incomplete penetration defects. It has been verified that the predictions of both models exhibit a high degree of agreement with the simulation results, indicating that both models had provided accurate predictions for the pipeline's ultimate bearing capacity, which could be applied in experimental and practical engineering scenarios. • The mechanical response of X80 pipelines with incomplete penetration defects under complex loading was investigated. • The effects of various factors on the pipeline’s mechanical response and their relative importance were analyzed. • A failure prediction model was established by combining a neural network approach with a fitting formula.
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