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Concrete Crack Detection from Video Footage for Structural Health Monitoring

Part of the Lecture Notes in Civil Engineering book series (LNCE,volume 127)

Abstract

Non-destructive imaging is largely encouraged as a preliminary investigation for damage identification on concrete structural surfaces. Cracks are basic signatures for any structure to initiate the damage. As the whole world is currently connected with lot of cameras all around for various purposes either it be for traffic studies, accident analysis, thefts, natural or human disasters. Alternatively, the same video frames obtained from cameras located in or on the structure can be analysed even for the structural health monitoring. This study aims at identifying the cracks from images mined out of the video frames apart from the crack propagation and length of the crack. Convolution Neural Network is used to train over the images from the video captured during the laboratory compressive strength experiment on a concrete cube to examine and estimate the crack properties. This methodology can be extended to the real-life scenario to alert the damages caused in the structures.

Keywords

  • Crack detection
  • Convolution Neural Network
  • Image processing
  • Non-destructive method
  • Video footage

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Correspondence to Sushmita Kadarla .

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Kadarla, S., Beeram, S.K., Kalapatapu, P., Pasupuleti, V.D.K. (2021). Concrete Crack Detection from Video Footage for Structural Health Monitoring. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2020. Lecture Notes in Civil Engineering, vol 127. Springer, Cham. https://doi.org/10.1007/978-3-030-64594-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-64594-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64593-9

  • Online ISBN: 978-3-030-64594-6

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