Translator Disclaimer
Presentation
24 April 2020 Corrosion-induced damage detection and conditional assessment for metallic civil structures using machine learning approaches (Conference Presentation)
Author Affiliations +
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
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
PRESENTATION


SHARE
Advertisement
Advertisement
Back to Top