Stochastic Filter-based Fatigue Crack Growth Prediction for Pipelines considering Unknown Model Parameters and Measurement Uncertainty-Reference-Cited by-同舟云学术

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Stochastic Filter-based Fatigue Crack Growth Prediction for Pipelines considering Unknown Model Parameters and Measurement Uncertainty

Published:2021-11 Issue: Volume: Page:
ISSN:2667-1433
Container-title:Journal of Pipeline Science and Engineering
language:en
Short-container-title:Journal of Pipeline Science and Engineering

Author:

Bartaula Durlabh,Adeeb Samer,Li Yong

Publisher

Elsevier BV

Reference22 articles.

1. Fatigue Considerations for Natural Gas Transmission Pipelines;Semiga;BMT Fleet Technology Limited, The Interstate Natural Gas Association of America (INGAA),2016

2. 579: Recommended Practice for Fitness for Service;API,2016

3. Guidance on methods for assessing the acceptability of flaws in metallic structures;BS,2015

4. Application of deterministic resampling particle filter to fatigue prognosis;Yang;Journal of Vibroengineering,2017

5. Monte Carlo-based filtering for fatigue crack growth estimation;Cadini;Probabilistic Engineering Mechanics,2009

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. XGBoost algorithm-based prediction of safety assessment for pipelines;International Journal of Pressure Vessels and Piping;2022-06

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