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Open AccessArticle

Automated Transverse Crack Mapping System with Optical Sensors and Big Data Analytics

1
Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA
2
Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Omaha, NE 68182, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 1838; https://doi.org/10.3390/s20071838
Received: 31 January 2020 / Revised: 17 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
Transverse cracks on bridge decks provide the path for chloride penetration and are the major reason for deck deterioration. For such reasons, collecting information related to the crack widths and spacing of transverse cracks are important. In this study, we focused on developing a data pipeline for automated crack detection using non-contact optical sensors. We developed a data acquisition system that is able to acquire data in a fast and simple way without obstructing traffic. Understanding that GPS is not always available and odometer sensor data can only provide relative positions along the direction of traffic, we focused on providing an alternative localization strategy only using optical sensors. In addition, to improve existing crack detection methods which mostly rely on the low-intensity and localized line-segment characteristics of cracks, we considered the direction and shape of the cracks to make our machine learning approach smarter. The proposed system may serve as a useful inspection tool for big data analytics because the system is easy to deploy and provides multiple properties of cracks. Progression of crack deterioration, if any, both in spatial and temporal scale, can be checked and compared if the system is deployed multiple times. View Full-Text
Keywords: optical sensor; computer vision; big data pipeline; automated transverse crack mapping; bridge deck optical sensor; computer vision; big data pipeline; automated transverse crack mapping; bridge deck
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Won, K.; Sim, C. Automated Transverse Crack Mapping System with Optical Sensors and Big Data Analytics. Sensors 2020, 20, 1838.

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