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Experimental Study on Digital Image Correlation for Deep Learning-Based Damage Diagnostic

  • Nur Sila GulgecEmail author
  • Martin Takáč
  • Shamim N. Pakzad
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Large quantities of data which contain detailed condition information over an extended period of time should be utilized to prioritize infrastructure repairs. As the temporal and spatial resolution of monitoring data drastically increase by advances in sensing technology, structural health monitoring applications reach the thresholds of big data. Deep neural networks are ideally suited to use large representative training datasets to learn complex damage features. In the previous study of authors, a real-time deep learning platform was developed to solve damage detection and localization challenge. The network was trained by using simulated structural connection mimicking the real test object with a variety of loading cases, damage scenarios, and measurement noise levels for successful and robust diagnosis of damage. In this study, the proposed damage diagnosis platform is validated by using temporally and spatially dense data collected by Digital Image Correlation (DIC) from the specimen. Laboratory testing of the specimen with induced damage condition is performed to evaluate the performance and efficiency of damage detection and localization approach.

Keywords

Structural health monitoring Digital image correlation Convolutional neural networks Damage detection 

Notes

Acknowledgements

Research funding is partially provided by the National Science Foundation through Grant No. CMMI-1351537 by Hazard Mitigation and Structural Engineering program, and by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA). Martin Takáč was supported by National Science Foundation grant CCF-1618717 and CMMI-1663256.

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Copyright information

© Society for Experimental Mechanics, Inc. 2020

Authors and Affiliations

  • Nur Sila Gulgec
    • 1
    Email author
  • Martin Takáč
    • 2
  • Shamim N. Pakzad
    • 1
  1. 1.Department of Civil and Environmental EngineeringLehigh University, Imbt LabsBethlehemUSA
  2. 2.Department of Industrial and Systems EngineeringLehigh University, Harold S. Mohler LaboratoryBethlehemUSA

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