DOI QR코드

DOI QR Code

Application of an image processing-based algorithm for river-side granular sediment gradation distribution analysis

  • Azarafza, Mohammad (Department of Civil Engineering, University of Tabriz) ;
  • Nanehkaran, Yaser A. (School of Information Engineering, Jiangxi University of Science and Technology) ;
  • Akgun, Haluk (Geotechnology Unit, Department of Geological Engineering, Middle East Technical University (METU)) ;
  • Mao, Yimin (School of Information Engineering, Jiangxi University of Science and Technology)
  • 투고 : 2020.06.21
  • 심사 : 2021.08.02
  • 발행 : 2021.09.25

초록

Determining grain-size and grading distribution of river-side sediments is very important for issues related to lateral embankment drift, river-side nourishment, management plans, and riverbank stability. In this regard, experimental procedures such as sieve analysis are used in regular assessments which require special laboratory equipment that are quite time consuming to perform. The presented study provides a machine vision and image processing-based approach for determining coarse grained sediment size and distribution that is relatively quick and effective. In this regard, an image image processing-based method was used to determine the particle size of sediments as justified by screening tests which were conducted on samples taken from the riverside granular sediments. As a methodology, different grain identification stages were applied to extract sediment features such as pre-processing, edge detection, granular size classification and post-processing. According to the results of the grain identification stages, the applied technique identified about 35% sand, 55% gravel and 7% cobble which is approximately near to the screen test results which were determined as 30% sand, 52% gravel, and 5% cobble. These results obtained from computer-based analyses and experiments indicated that the utilised processing technique provided satisfactory results for gradation distribution analysis regarding riverside granular sediments.

키워드

참고문헌

  1. AASHTO T88 (2013), Standard Method of Test for Particle Size Analysis of Soils, American Association of State Highway and Transportation Officials, Washington, USA.
  2. ASTM D422 (2006), Standard test methods for particle size analysis of soils, ASTM International, West Conshohocken, PA, USA
  3. Azarafza, M. and Asghari-Kaljahi, E. (2016), Applied Geotechnical Engineering, Negarkhane Publication, Isfahan, Iran. [in Persian]
  4. Azarafza, M., Feizi-Derakhshi, M.R. and Jeddi, A. (2017), "Blasting pattern optimization in open-pit mines by using the genetic algorithm", J. Geotech. Geol., 13(2), 75-81.
  5. Azarafza, M., Ghazifard, A., Akgun, H. and Asghari-Kaljahi, E. (2019), "Development of a 2D and 3D computational algorithm for discontinuity structural geometry identification by artificial intelligence based on image processing techniques", Bull. Eng. Geol. Environ., 78(5), 3371-3383. https://doi.org/10.1007/s10064-018-1298-2
  6. Barnard, P.L., Rubin, D.M., Harney, J. and Mustain, N. (2007), "Field test comparison of an autocorrelation technique for determining grain size using a digital 'beachball' camera versus traditional methods", Sediment. Geol., 201, 180-195. https://doi.org/10.1016/j.sedgeo.2007.05.016
  7. Becker, L.W.M., Hjelstuen, B.O., Storen, E.W.N. and Sejrup, H.P. (2018), "Automated counting of sand-sized particles in marine records", Sediment. Banner, 65(3), 842-850. https://doi.org/10.1111/sed.12407
  8. Boggs Jr, S. (2011), Principles of Sedimentology and Stratigraphy, Pearson, New York, NY, USA.
  9. Budhu, M. (2010), Soil Mechanics and Foundations, (3rd Edition), Wiley, New Jersey, USA.
  10. Buscombe, D. (2008), "Estimation of grain-size distributions and associated parameters from digital images of sediment", Sediment. Geol., 210, 1-10. https://doi.org/10.1016/j.sedgeo.2008.06.007
  11. Buscombe, D. and Masselink, G. (2009), "Grain size information from the statistical properties of digital images of sediment", Sediment., 56, 421-438. https://doi.org/10.1111/j.1365-3091.2008.00977.x
  12. Carlsson, O. and Nyberg, L. (1981), "A method for estimation of fragment size distribution with automatic image processing", Proceedings of the 1st International Symposium on Rock Fragmentation by Blasting, Roger Holmberg, August.
  13. Cassel, M., Piegay, H., Lave, J., Vaudor, L., Hadmoko, S.D., Budi, S.W. and Lavigne, F. (2018), "Evaluating a 2D image-based computerized approach for measuring riverine pebble roundness", Geomorph., 311, 143-157. https://doi.org/10.1016/j.geomorph.2018.03.020
  14. Charpentier, I., Sarocchi, D. and Sedano, L.A.R. (2013), "Particle shape analysis of volcanic clast samples with the Matlab tool MORPHEO", Comput. Geosci., 51, 172-181. https://doi.org/10.1016/j.cageo.2012.07.015
  15. Chavez, G.M., Sarocchi, D., Arce Santana, E. and Borselli, L. (2015), "Optical granulometric analysis of sedimentary deposits by color segmentation-based software: OPTGRAN-CS", Comput. Geosci., 85(A), 248-257. https://doi.org/10.1016/j.cageo.2015.09.007
  16. Chen, T., Kuo, C.F. and Chen, J.C.Y. (2019), "Computer vision monitoring and detection for landslides", Struct. Monit. Maint., Int. J., 6(2), 161-171. https://doi.org/10.12989/smm.2019.6.2.161
  17. Davies, E.R. (2012), Computer and Machine Vision: Theory, Algorithms, Practicalities, (4th Edition), Academic Press, MA, USA.
  18. Dill, H.G., Buzatu, A., Balaban, S.J., Ufer, K., Techmer, A., Schedlinsky, W. and Fussl, M. (2020), "The transition of very coarse-grained meandering to straight fluvial drainage systems in a tectonized foreland-basement landscape during the Holocene (SE Germany) - A joint geomorphological-geological study", Geomorphology, 370, 107364. https://doi.org/10.1016/j.geomorph.2020.107364
  19. Dipova, N. (2017), "Determining the grain size distribution of granular soils using image analysis", Acta Geotech. Slovenica, 14(1), 29-37.
  20. Faramarzi, F., Mansouri, H. and Farsangi, M.E. (2013), "A rock engineering systems based model to predict rock fragmentation by blasting", Int. J. Rock. Mech. Min. Sci., 60, 82-94. https://doi.org/10.1016/j.ijrmms.2012.12.045
  21. Frydrych, M., Rdzany, Z. and Petera-Zganiacz, J. (2019), "The problem of analysing grain size distribution in fluvioglacial coarse-grained sediments", Proceedings of the State International Field Symposium of the Peribaltic Working Group, Greifswald, Germany, September.
  22. Gonzalez, R.C., Woods, R.E. and Steven, L. (2010), Digital Image Processing using MATLAB, (2nd Edition), McGraw-Hill Education, New York, NY, USA.
  23. Griffiths, J.C. (1961), "Measurement and properties of sediments", J. Geol., 69, 487-498. https://doi.org/10.1086/626767
  24. Honakanen, M., Saarenrinne, P., Stoor, T. and Niinimaki, J. (2005), "Recognition of highly overlapping ellipse-like bubble images", Measur. Sci. Technol., 16, 1760-1770. https://doi.org/10.1088/0957-0233/16/9/007
  25. Korath, J., Abbas, A. and Romagnoli, J. (2007), "Separating touching and overlapping objects in particle images - A combined approach", Chem. Eng. Trans., 11, 167-172.
  26. Krishna, B.M., Tezeswi, T.P., Kumar, P.R., Gopikrishna, K., Sivakumar, M.V.N. and Shashi, M. (2019), "QR code as speckle pattern for reinforced concrete beams using digital image correlation", Struct. Monit. Maint., Int. J., 6(1), 67-84. https://doi.org/10.12989/smm.2019.6.1.067
  27. Latham, J.P., Kemeny, J., Maerz, N., Noy, M., Schlifer, J. and Tose, S. (2003), "A blind comparison between results of four image analysis systems using a photo-library of piles of sieved fragments", Int. J. Rock. Fragment. Blast., 7(2), 105-132.
  28. Liu, Y., Nadolski, S., Elmo, D., Klein, B. and Scoble, M. (2015), "Use of digital imaging processing techniques to characterize block caving secondary fragmentation and implications for proposed cave-to-mill approach", Proceedings of the 49th US Rock Mechanics/Geomechanics Symposium, San Francisco, CA, USA, June.
  29. Maiti, A., Chakravarty, D., Biswas, K. and Halder, A. (2017), "Development of a mass model in estimating weight-wise particle size distribution using digital image processing", Int. J. Min. Sci. Technol., 27(3), 435-443. https://doi.org/10.1016/j.ijmst.2017.03.015
  30. Pont-Tuset, J., Arbelaez, P., Barron, J.T., Marques, F. and Malik, J. (2017), "Multiscale combinatorial grouping for image segmentation and object proposal generation", IEEE Trans. Pattern. Anal. Mach. Intell., 39, 128-140. https://doi.org/10.1109/TPAMI.2016.2537320
  31. Qiao, P. and Fan, W. (2014), "Lamb wave-based damage imaging method for damage detection of rectangular composite plates", Struct. Monit. Maint., Int. J., 1(4), 411-425. https://doi.org/10.12989/smm.2014.1.4.411
  32. Rubin, D.M. (2004), "A simple autocorrelation algorithm for determining grain size from digital images of sediment", J. Sediment. Res., 74, 160-165. https://doi.org/10.1306/052203740160
  33. Shen, L., Song, X., Iguchi, M. and Yamamoto, F. (2000), "A method for recognizing particles in overlapped particle images", Pattern Recogn. Let., 21, 21-30. https://doi.org/10.1016/S0167-8655(99)00130-0
  34. Smith, Z.D. and Maxwell, D.J. (2021), "Constructing vertical measurement logs using UAV-based photogrammetry: Applications for multiscale high-resolution analysis of coarse-grained volcaniclastic stratigraphy", J. Volcan. Geotherm. Res., 409, 107122. https://doi.org/10.1016/j.jvolgeores.2020.107122
  35. Solomon, C. and Breckon, T. (2011), Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab, Wiley, NJ, USA.
  36. Sonka, M., Hlavac, V. and Boyle, R, (2014), Image Processing, Analysis, and Machine Vision, (4th Edition), Cengage Learning, Boston, MA, USA.
  37. Uijlings, J., van de Sande, K., Gevers, T. and Smeulders, A. (2013), "Selective search for object recognition", Int. J. Comput. Vision, 104, 154-171. https://doi.org/10.1007/s11263-013-0620-5
  38. Wen, Y., Chen, Z., Zhang, G., Wang, Y., Hao, J. and Zhang Q. (2021), "A Rapid Gradation Detection System for Earth and Stone Materials Based on Digital Image", Adv. Civil Eng., 2021, 6660301. https://doi.org/10.1155/2021/6660301
  39. Wood, D.M. (1991), Soil Behaviour and Critical State Soil Mechanics, Cambridge University Press, Cambridge, UK.
  40. Xi, P.S., Ye, X.W., Jin, T. and Chen, B. (2018), "Structural performance monitoring of an urban footbridge", Struct. Monit. Maint., Int. J., 5(1), 129-150. https://doi.org/10.12989/smm.2018.5.1.129
  41. Yarahmadi, R., Bagherpour, R., Sousa, L.M.O. and Taherian, S. (2015), "How to determine the appropriate methods to identify the geometry of in situ rock blocks in dimension stones", Environ. Earth Sci., 74, 6779-6790. https://doi.org/10.1007/s12665-015-4672-4
  42. Ye, X.W., Jin, T. and Yun, C.B. (2019), "A review on deep learning-based structural health monitoring of civil infrastructures", Smart Struct. Syst., Int. J., 24(5), 567-585. https://doi.org/10.12989/sss.2019.24.5.567
  43. Zomorodian, S.M.A., Ataee Naghab, M.J., Zolghadr, M. and O'Kelly, B.C. (2020), "Overtopping erosion of model earthen dams analysed using digital image-processing method", Water Manage., 137(6), 304-316. https://doi.org/10.1680/jwama.19.00098

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