A data-driven prediction model for maximum pitting corrosion depth of subsea oil pipelines using SSA-LSTM approach

Author:

Li Xinhong,Guo Mengmeng,Zhang Renren,Chen Guoming

Publisher

Elsevier BV

Subject

Ocean Engineering,Environmental Engineering

Reference28 articles.

1. External corrosion pitting depth prediction using Bayesian spectral analysis on bare oil and gas pipelines;Balekelayi;Int. J. Pres. Ves. Pip.,2020

2. Modelling of pitting corrosion in marine and offshore steel structures–A technical review;Bhandari;J. Loss Prev. Process. Ind.,2015

3. Particle swarm optimization for solving a scan-matching problem based on the normal distributions transform;Bouraine;Evol. Intell.,2021

4. The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate;Chou;Eng. Appl. Artif. Intell.,2017

5. A simulation-based Bayesian approach to predict the distribution of maximum pit depth in steam generator tubes;Hazra;Nucl. Eng. Des.,2021

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