Deep Learning Enriched Automation in Damage Detection for Sustainable Operation in Pipelines with Welding Defects under Varying Embedment Conditions

Deep Learning Enriched Automation in Damage Detection for Sustainable Operation in Pipelines with Welding Defects under Varying Embedment Conditions

Welded joints in metallic pipelines and other structures are used to connect metallic structures. Welding defects, such as cracks and lack of fusion, are vulnerable to initiating early-age cracking and corrosion. The present damage identification techniques use ultrasonic-guided wave procedures, whi...

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Bibliographic Details
Main Authors: Li Shang, Zi Zhang, Fujian Tang, Qi Cao, Nita Yodo, Hong Pan, Zhibin Lin
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Computation
Subjects:
deep learning approaches
damage detection
ultrasonic-guided wave
welding defect
non-destructive testing
embedment
Online Access:https://www.mdpi.com/2079-3197/11/11/218

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