Browse > Article
http://dx.doi.org/10.12989/sss.2018.22.2.209

Efflorescence assessment using hyperspectral imaging for concrete structures  

Kim, Byunghyun (Department of Civil Engineering, University of Seoul)
Cho, Soojin (Department of Civil Engineering, University of Seoul)
Publication Information
Smart Structures and Systems / v.22, no.2, 2018 , pp. 209-221 More about this Journal
Abstract
Efflorescence is a phenomenon primarily caused by a carbonation process in concrete structures. Efflorescence can cause concrete degradation in the long term; therefore, it must be accurately assessed by proper inspection. Currently, the assessment is performed on the basis of visual inspection or image-based inspection, which may result in the subjective assessment by the inspectors. In this paper, a novel approach is proposed for the objective and quantitative assessment of concrete efflorescence using hyperspectral imaging (HSI). HSI acquires the full electromagnetic spectrum of light reflected from a material, which enables the identification of materials in the image on the basis of spectrum. Spectral angle mapper (SAM) that calculates the similarity of a test spectrum in the hyperspectral image to a reference spectrum is used to assess efflorescence, and the reference spectral profiles of efflorescence are obtained from theUSGS spectral library. Field tests were carried out in a real building and a bridge. For each experiment, efflorescence assessed by the proposed approach was compared with that assessed by image-based approach mimicking conventional visual inspection. Performance measures such as accuracy, precision, and recall were calculated to check the performance of the proposed approach. Performance-related issues are discussed for further enhancement of the proposed approach.
Keywords
hyperspectral imaging; efflorescence; concrete; spectral angle mapper;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Santos, B.O., Valenca, J., and Julio, E. (2017), Detection of cracks on concrete surfaces by hyperspectral image processing. In Automated Visual Inspection and Machine Vision II (Vol. 10334, p. 1033407). International Society for Optics and Photonics. Munich, Germany, June.
2 Taylor, H.F. (1997), Cement Chemistry. Thomas Telford Ltd. London, UK
3 USGS Spectral Library (2017), Version 7, available at: https://speclab.cr.usgs.gov/spectral-lib.html.
4 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. and Dobson, R. (2011), "Evaluation of commercially available remote sensors for highway bridge condition assessment", J. Bridge Eng., 17(6), 886-895.   DOI
5 van der Werff, H.M.A. (2006), Knowledge-based Remote Sensing of Complex Objects: Recognition of Spectral and Spatial Patterns Resulting from Natural Hydrocarbon Seepages. Utrecht University, Utrecht, Utrecht, Netherlands
6 Zhang, C., Ye, H., Liu, F., He, Y., Kong, W. and Sheng, K. (2016), "Determination and visualization of pH values in anaerobic digestion of water hyacinth and rice straw mixtures using hyperspectral imaging with wavelet transform denoising and variable selection", Sensors, 16(2), 244.   DOI
7 Baseley, D., Wunderlich, L., Phillips, G., Gross, K., Perram, G., Willison, S., Phillips, R., Magnuson, M., Lee, S.D. and Harper, W.F. (2016), "Hyperspectral analysis for standoff detection of dimethyl methylphosphonate on building materials", Build. Environ., 108, 135-142.   DOI
8 Adhikari, R.S., Bagchi A. and Moselhi O. (2014), "Automated condition assessment of concrete bridges with digital imaging", Smart Struct. Syst., 13(6), 901-925.   DOI
9 Arita, J., Sasaki, K.I., Endo, T. and Yasuoka, Y. (2001). "Assessment of concrete degradation with hyper-spectral remote sensing", Proceedings of the 22nd Asian Conference on Remote Sensing, Singapore, November.
10 BASF (2014), Efflorescence Guidelines - causes, prevention, removal, control; BASF, Ludwigshafen, Germany: www.basf.com
11 Bateson, A. and Curtiss, B. (1996), "A method for manual endmember selection and spectral unmixing", Remote Sens. Environ., 55(3), 229-243.   DOI
12 Cemex USA - Technical Bulletin (2008), Efflorescence in Concrete Products, Houston, Texas, USA: available at http://www.cemexusa.com/ProductsServices/files/TechnicalServices/Efflorescence_in_Concrete_Products.pdf.
13 Caughlin, T.T., Graves, S.J., Asner, G.P., Breugel, M., Hall, J.S., Martin, R.E. and Bohlman, S.A. (2016), "A hyperspectral image can predict tropical tree growth rates in single-species stands", Ecological Appl., 26(8), 2367-2373.
14 Chang, C.I. (2003), Techniques for Spectral Detection and Classification, Kluwer Academic/ Plenum Publishers, New York, USA.
15 Grahn. H. and Geladi. P. (2007), Techniques and Applications of Hyperspectral Image Analysis. John Wiley & Sons., NJ, USA.
16 Dawood, T., Zhu, Z., and Zayed, T. (2017), "Machine vision-based model for spalling detection and quantification in subway networks", Autom. Constr., 81, 149-160.   DOI
17 Dow, C. and Glasser, F.P. (2003), "Calcium carbonate efflorescence on Portland cement and building materials", Cement Concrete Res., 33(1), 147-154.   DOI
18 ElMasry, G., Sun, D.W. and Allen, P. (2012), "Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef", J. Food Eng., 110(1), 127-140.   DOI
19 Harris Geospatial Solutions (2017), Material Identification Using ENVI. Available at: https://www.harrisgeospatial.com/docs/THORMaterialIdentification.html.
20 Jun, S., Xin, Z., Hanping, M., Xiaohong, W., Xiaodong, Z. and Hongyan, G. (2016), "Identification of pesticide residue level in lettuce based on hyperspectra and chlorophyll fluorescence spectra",. Int. J. Agricultural Biol. Eng., 9(6), 231.
21 Ministry of Land, Infrastructure, and Transport (MOLIT) (2016), Detailed Guidelines of Safety Inspection and Precise Safety Diagnosis for Bridges (in Korean).
22 Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., Driscoll, R.L. and Klein, A.J. (2017), USGS Spectral Library Version 7: U.S. Geological Survey Data Series 1035, 61 p.
23 Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J. and Goetz, A.F.H. (1993), "The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data", Remote Sens. Environ., 44(2-3), 145-163.   DOI
24 Lee, J.D., Dewitt, B.A., Lee, S.S., Bhang, K.J. and Sim, J.B. (2012), "Analysis of concrete reflectance characteristics using spectrometer and VNIR hyperspectral camera", Int. Arch. Photogrammetry Remote Sens. Spatial Inform. Sci., 39, B7.
25 Liu, Y., Cho, S., Spencer Jr, B.F. and Fan, J. (2014), "Automated assessment of cracks on concrete surfaces using adaptive digital image processing", Smart Struct. Syst., 14(4), 719-741.   DOI
26 Man, S.H., Chang, C.C., Hassan, M. and Bermak, A. (2015), "Design and calibration of a wireless laser-based optical sensor for crack propagation monitoring", Smart Struct. Syst., 15(6), 1543-1567   DOI
27 Proto, M., Bavusi, M., Bernini, R., Bigagli, L., Bost, M., Bourquin, F., Cottineau, L.M., Cuomo, V., Vecchia, P.D., Dolce, M. and Dumoulin, J. (2010), "Transport infrastructure surveillance and monitoring by electromagnetic sensing: the ISTIMES project", Sensors, 10(12), 10620-10639.   DOI