Assessment of Concrete Efflorescence based on Hyper-spectral Imaging

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Published Jul 14, 2017
Byunghyun Kim Dae-Myung Kim Soojin Cho

Abstract

Efflorescence is a phenomenon mostly made by carbonation process of concrete structures. It is one of the internal damages seriously considered in evaluating the durability of concrete bridges. In Korea, the guideline for the bridge safety inspection requests to assess crack, efflorescence, spalling and reinforcement exposure in prior for the slabs and girders of concrete bridges. Currently, the assessment is performed based on the visual inspection of expertized engineers, which may result in subjective inspection result. In this study, a novel method to assess concrete efflorescence is proposed based on hyper-spectral imaging (HSI) device. The HSI acquires the light intensity for a number of continuous spectral bands of light for each pixel in an image, which makes the HSI provides more detailed information than a normal color camera that collects intensity for only three bands corresponding to the colors, RGB (red, green, and blue). A stepwise assessment algorithm is developed based on the spectral features developed to decompose efflorescence area from the inspected concrete area. The algorithm is verified in the laboratory testing using concrete specimens with the efflorescence, which shows high accuracy of the proposed HSI-based assessment.

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Keywords

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References
Arita, J., Sasaki, K. I., Endo, T., & Yasuoka, Y. (2001). Assessment of concrete degradation with hyper-spectral remote sensing. In Paper presented at the 22nd Asian Conference on Remote Sensing (Vol. 5, p. 9).
Baseley, D., Wunderlich, L., Phillips, G., Gross, K., Perram, G., Willison, S., Phillips, R., Magnuson, M., Lee, S. D. & Harper, W. F. (2016). Hyperspectral analysis for standoff detection of dimethyl methylphosphonate on building materials. Building and Environment, 108, 135-142.
Caughlin, T. T., Graves, S. J., Asner, G. P., Breugel, M., Hall, J. S., Martin, R. E. & Bohlman, S. A. (2016). A hyperspectral image can predict tropical tree growth rates in single‐species stands. Ecological Applications, 26(8), 2367-2373.
Chang, C-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer Science & Business Media. ISBN 978-0-306-47483-5. Berlin, Germany.
ElMasry, G., Sun, D. W., & Allen, P. (2012). Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food
Engineering, 110(1), 127-140.
Grahn, H., & Geladi, P. (2007). Techniques and Applications of Hyperspectral Image Analysis. John Wiley & Sons. ISBN 978-0-470-01087-7. NJ, USA.
Lee, J. D., Dewitt, B. A., Lee, S. S., Bhang, K. J., & Sim, J. B. (2012). Analysis of concrete reflectance characteristics using spectrometer and VNIR hyperspectral camera. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, B7.
Lu, G., & Fei, B. (2014). Medical hyperspectral imaging: a review. Journal of biomedical optics, 19(1), 010901-010901.
Michael K., Stefan I. & Nicoletta Z. (2007) Efflorescence, Zurich, Switzerland, BASF Construction Chemicals Europe AG.
Vaghefi, K., Oats, R. C., Harris, D. K., Ahlborn, T. T. M., Brooks, C. N., Endsley, K. A., Roussi, C., Shuchman, R., Burns, J. W., & Dobson, R. (2011). Evaluation of commercially available remote sensors for highway bridge condition assessment. Journal of Bridge Engineering, 17(6),
886-895.
Section
Special Session Papers