Machine learning-assisted intelligent interpretation of distributed fiber optic sensor data for automated monitoring of pipeline corrosion

Author:

Liu YimingORCID,Tan XiaoORCID,Bao Yi

Funder

National Science Foundation

U.S. Department of Transportation

Pipeline and Hazardous Materials Safety Administration

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference37 articles.

1. A review on corrosion detection and protection of existing reinforced concrete (RC) structures;Hu;Constr. Build. Mater.,2022

2. NACE international’s IMPACT study breaks new ground in corrosion management research and practice;Jacobson;The Bridge,2016

3. Statistical analysis of incidents on onshore CO2 pipelines based on PHMSA database;Vitali;J. Loss Prev. Process Ind.,2022

4. Review on automated condition assessment of pipelines with machine learning;Liu;Adv. Eng. Inf.,2022

5. Interpretation of cyclic potentiodynamic polarization test results for study of corrosion behavior of metals: A review;Esmailzadeh;Prot. Met. Phys. Chem,2018

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