Corrosion-induced damage detection and conditional assessment for metallic civil structures using machine learning approaches (Conference Presentation)

Zhibin Lin; Hong Pan; Zi Zhang; Fujian Tang; Xingyu Wang

doi:10.1117/12.2560131

24 April 2020 Corrosion-induced damage detection and conditional assessment for metallic civil structures using machine learning approaches (Conference Presentation)

Zhibin Lin, Hong Pan, Zi Zhang, Fujian Tang, Xingyu Wang

Author Affiliations +

Proceedings Volume 11379, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020; 113792F (2020) https://doi.org/10.1117/12.2560131
Event: SPIE Smart Structures + Nondestructive Evaluation, 2020, Online Only

Abstract

corrosion still responds for huge maintenance cost of nationwide civil structures. In this study, we explored a machine learning approach to extract information from sensory data for early-age corrosion-induced damage identification and classification. Lamb-wave guided signals of steel samples collected from simulated corrosion damage were used for model training and calibration. The results showed that the machine learning method allowed effective information fusion for early-age corrosion.

Conference Presentation

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Zhibin Lin, Hong Pan, Zi Zhang, Fujian Tang, and Xingyu Wang "Corrosion-induced damage detection and conditional assessment for metallic civil structures using machine learning approaches (Conference Presentation)", Proc. SPIE 11379, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020, 113792F (24 April 2020); https://doi.org/10.1117/12.2560131

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KEYWORDS

Machine learning

Corrosion

Damage detection

Information fusion

Sensors

Bridges

Calibration

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