A New Approach to Determine the Soil Particles Arrangement by the Digital Image Processing

Document Type : Original Article

Authors

1 Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

2 Department of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Abstract

Soil particle arrangement affects soil behavior. Determining the arrangement of soil particles is complex. In this paper, wavelet transformation based on digital image processing was developed to determine the soil particles arrangement. Soil image is decomposed to 512×512 pixels small zones and is analyzed by wavelet transformation. For each analysis zone, an energy index is calculated. Since the energy can be calculated discretely for horizontal, vertical and diagonal directions, more data about the soil particles arrangement such as particles shape, particles orientation, and fabric can be acquired. For this purpose, the energy index is determined by comparing horizontal and vertical energies. Imaging of soils is done by sediment imaging test and flat surface test, and the energy index is calculated and compared for both methods. Energy index values greater than zero indicate that the particles are horizontally arranged, while the energy index values below zero represent the vertical arrangement of the particles. Therefore, the energy index is an appropriate indicator for determining the soil particle arrangement. Determination of particles arrangement by the DIP method reduces the operator and decreases errors.

Keywords

Main Subjects

References

Azami, A., Pietruszczak, S., Guo, P., 2010. Bearing capacity of shallow foundations in transversely isotropic granular media. International Journal for Numerical and Analytical Methods in Geomechanics, 34(8): 771-793.
Bowman, E.T., Soga, K., Drummond, W., 2001. Particle shape characterisation using fourier descriptor analysis. Geotechnique, 51(6): 545-554.
Dipova, N., 2017. Determing the grain size distribution of granular soils using image analysis. Acta Geotechnica Slovenica, 14: 29-37.
Du, C.J., Sun, D.W., 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology, 15(5): 230-249.
Eremin, S.N., 2006. Image processing technology in the systems for quality control of sheet metal  roll. Pattern Recognition and Image Analysis, 16(1): 127-130.
Haar, A., 1910. The theory of orthogonal function systems. Mathematische Annalen, 69(3): 331-371(in Germen).
Islam, M.J., Ahmadi, M., Sid-Ahmed, M.A., 2008. Image processing techniques for quality inspection of gelatin capsules in pharmaceutical applications. The 10th International Conference on Control, Automation, Robotics and Vision.
Kim, F.H., Penumadu, D., Gregor, J., Kardjilov, N., Manke, I., 2013. High resolution neutron and x-ray imaging of granular materials. Journal of Geotechnical and Geoenvironmental Engineering, 139(5): 715-723.
Kim, F.H., Penumadu, D., Hussey, D.S., 2012. Water distribution variation in partially saturated granular materials using neutron imaging. Journal of Geotechnical and Geoenvironmental Engineering, 138(2): 147-154.
Kumara, G.H.A.J.J., Hayano, K., Ogiwara, K., 2012. Image analysis techniques on evaluation of particle size distribution of gravel. International Journal of Geomate, 3(1): 290-297.
Li, X.S., Dafalias, Y.F., 2012. Anisotropic critical state theory: role of fabric. Journal of Engineering Mechanics, 138(3): 263-275.
Luo, D., Macleod J.E.S., Leng, X., Smart, P., 1992. Automatic orientation analysis of particles of soil microstructures. Géotechnique, 42(1): 97-107.
Mahmoud, E., Masad, E., Nazarian, S., 2010. Discrete element analysis of the influences of aggregate properties and internal structure on fracture in asphalt mixtures. Journal of Materials in Civil Engineering, 22(1):10-20.
Mardia, K.V., Baczkowski, A.J., Feng, X., Hainsworth, T.J., 1997. Statistical methods for automatic interpretation of digitally scanned finger prints. Pattern Recognition Letters, 18(11-13): 1197-1203.
Ohm, H.S. and Hryciw, R.D., 2013. Enhanced soil characterization through advances in imaging technology. Proceedings of the 18th International Conference on Soil Mechanics and Geotechnical Engineering: 3491-3494.
Pham, D.L., Xu, C., Prince, J.L., 2000. Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2: 315-337.
Sochan, A., Zielińsk, P., Bieganowski, A., 2015. Selection of shape parameters that differentiate sand grains, based on the automatic analysis of two-dimensional images. Sedimentary Geology, 327: 14-20.
Tabrizi-zarringhabaei, S., Ejlali, R.G., Yousefzadeh Fard, M., Seyyedfattahi, S., 2019. An image-based method to determine the particle size distribution (PSD) of fine-grained soil. The Mining-Geology-Petroleum Engineering Bulletin, 34(3): 81-88.
Tafesse, S., Fernlund, J.M.R., Bergholm, F., 2012. Digital sieving-Matlab based 3-D image analysis. Engineering Geology, 137-138: 74-84.
Wang, W.H., Liu, X.Y., Sun, Y., 2009. High-throughput automated injection of individual biological cells. IEEE Transactions on Automation Science and Engineering, 6(2): 209-219.
Wilson, J.D., Klotz, L.D., 1996. Quantitative analysis of aggregate based on hough transform. Transportation Research Record: Journal of the Transportation Research Board, 1530(1): 111-115.
Yu, H., Zeng, X., Li, B., Ming, H., 2013. Effect of fabric anisotropy on liquefaction of sand. Journal of Geotechnical and Geoenvironmental Engineering, 139(5): 765-774.

Files

History

  • Receive Date: 01 May 2019
  • Revise Date: 14 August 2019
  • Accept Date: 24 September 2019

Share

How to cite

Statistics