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An alternative method for the particle size distribution: Image processing

Year 2023, Volume: 7 Issue: 2, 108 - 115, 15.04.2023
https://doi.org/10.31127/tuje.1053462

Abstract

Granular soils are used in different areas of civil engineering due to their easy accessibility and low cost as a material. The sieve analysis method, which has been conducted in rock physics measurements for many years, has been practicing determining the particle size distributions for those materials. In this study, a method based on image analysis technique has been developed as an alternative to traditional sieve analysis method for determining the particle size distribution in granular soils. This technique based on image processing to determine particle distributions via the experimental setup that consist of a camera, tripod, light box and mechanical shaking apparatus. In order to assess the reliability of this technique, each sample was subjected to traditional sieve analysis and the results of both analysis methods were compared. In conclusion, it was observed that the results obtained with the image processing technique had a minimum 95.22% closeness with the sieve analysis experiment data.

References

  • Mora, C., Kwan, A., Chan, H. (1998). Particle Size Distribution Analysis of Coarse Aggregate Using Digital Image Processing. Cement and Concrete Research, 28 (6), 921-932.
  • Sezer, A. (2008). Determination of Microapical Properties of Different Types of Soils Using Image Processing Techniques, PhD Thesis, İzmir: Ege University Graduate School of Sciences, Department of Civil Engineering (in Turkish).
  • Murthy, V. N. S. (2002). Geotechnical Engineering, Principles and Practices of Soil Mechanics and Foundation Engineering, CRC Press.
  • Obaidat, M. T., Al-Masaeid, H. R., Gharaybeh, F., & Khedaywi, T. S. (1998). An Innovative Digital Image Analysis Approach to Quantify the Percentage of Voids in Mineral Aggreagates of Bituminous Mixtures. Canadian Journal of Civil Engineering, 25:1041-1049.
  • Gaydecki, P., Silva, I., Fernandes, B. T., Yu, Z. Z. (2000). A Portable Inductive Scanning System for Imaging Steel-Reinforcing Bars Embedded Within Concrete. Sensors and Actuators, 84:25-32.
  • Soroushian, P., Elzafraney, M., Nossoni, A. (2003). Specimen preparation and image processing and analysis techniques for automated quantification of concrete microcracks and voids. Cement and Concrete Research, 33:1949-1962.
  • Soroushian, P., Elzafraney. M. (2005). Morphological operations, planar mathematical formulations, and stereological interpretations for automated image analysis of concrete microstructure. Cement & Concrete Composites, 27:823-833.
  • Felekoglu, B., Güllü, D. (2006). Use of optical microscope and image processing techniques in clinker analysis. IMO Technical Journal, 247, 3761-3770.
  • Sinha, S. K., & Fieguth, P. W. (2006). Automated detection of cracks in buried concrete pipe images. Automation in Construction, 15, 58-72.
  • Park, S. H., Kim, H. K., Morales, A., & Ko, S. J. (2007). Air-void analysis system of polished concrete using image processing. Journal of Applied Computer Science, 15 (2), 19-25.
  • Ozen, M. (2007). Investigation of Relationship Between Aggregate Shape Parameters and Concrete Strength Using Imaging Techniques, Master's Thesis, Ankara: Middle East Technical University, The Graduate School of Natural and Applied Sciences.
  • Comak, B., Beycioğlu, A., Başyigit, C., Kılınçarslan, S. (2011). Use of Image Processing Techniques in Concrete Technology. 6th International Advanced Technologies Symposium (IATS'11), Elazığ.
  • Dipova, N. (2017). Determining The Grain Size Distribution of Granular Soils Using Image Analysis. Acta Geotechnica Slovenica, 1, 29-37.
  • German, R. M. (1994). Powder Metallurgy Science, New Jersey: Metal Powder Industries Federation.
  • Yue, Z. Q., Bekking, W., Morin, I. (1995). Application of digital image processing to quantitative study of asphalt concrete microstructure. Transportation Research Record, 1492:53-60.
  • NG, T. T. (1999). Fabric Study of Granular Materials After Compaction. ASCE Journal of Engineering Mechanics, 125 (12), 1390-1394.
  • Masad, E., & Button, J. (2000). Unified Imaging Approach for Measuring Aggregate Angularity and Texture. Computer-Aided Civil and Infrastructure Engineering, 15(4), 273-280.
  • Mora, C. F., & Kwan, A. K. H. (2000). Sphericity, shape factor, and convexity measurement of coarse aggregate for concrete using digital image processing. Cement and concrete research, 30(3), 351-358.
  • Yue, Z. Q., Chen, S., & Tham, L. G. (2003). Finite element modeling of geomaterials using digital image processing, Computers and Geotechnics, 30, 375-397.
  • Rousan, T. M. A. (2004). Characterization of Aggregate Shape Properties Using a Computer Automated System, Phd Thesis, USA: Texas A&M University.
  • Alshibli, K., & Alsaleh, M. (2004). Characterizing surface roughness and shape of sands using digital microscopy. ASCE Journal of Computing in Civil Engineering, 18 (1), 36-45.
  • Yang, X. (2005). Three Dimensional Characterization of Inheret and Induced Sand Microstructure, Phd Thesis, Georgia Institute of Technology.
  • Hu, R., Yue, Z., Tham, L., & Wang, L. (2005). Digital image analysis of dynamic compaction effects on clay fills. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 131 (11), 1411-1422.
  • Edizer, E. (2006). Particle Size Distribution with Digital Image Processing Method, Master Thesis, Adana: Çukurova University, Institute of Science and Technology.
  • Vangla, P., Roy, N., Mendu, K., & Latha, G. M. (2014). Digital Image Analysis for the Determination of Size and Shape Parameters of Sand Grains. Golden Jubilee Conference of the IGS Bangalore Chapter, Geo Innovations, Bangalore India.
  • Ehsan, K. M., Hossain, M. R., Manzur, T., Shohag, A., & Tabassum, N. (2016). Particle Size Analysis by Image Processing Technique. 1st Bangladesh Civil Engineering Summit, Dhaka, Bangladesh.
  • Jahne, B. (2005). Digital Image Processing, Germany: Springer.
  • Gonzales, R. C., & Woods, R. E. (2001). Digital Image Processing, USA: Prentice Hall.
  • McAndrew, A. (2004). An Introduction to Digital Image Processing with MATLAB, Melbourne: Victoria University of Technology.
  • Gonzales, R. C., & Woods, R. E. (2008). Digital Image Processing, Upper Saddle River, NJ: Pearson Prentice Hall, 1-3.
  • Turkish Standards Institute TS 3530 EN 933-1/April (1999). Experiments for Geometric Properties of Aggregates Part 1: Determination of Particle Size Distribution-Sieving Method
  • Mertens, G, & Elsen, J. (2006). Use of computer assisted image analysis for the determination of the grain- size distribution of sands used in mortars. Cement and Concrete Research 36:1453-1459 DOI: 10.1016/j.cemconres.2006.03.004.
  • Fernlund, J. M. R., Zimmerman, R. W., & Kragic, D. (2007). Influence of volume/mass on grain-size curves and conversion of image-analysis size to sieve size. Engineering Geology, 90:124–137 DOI: 10.1016/j.enggeo.2006.12.007.
  • Allen, T. (1968). Particle size measurement, Chapman and Hall, London.
  • Pettijohn, F. J. (1984). Sedimentary Rocks. S.K. Jain, India.
  • Fernlund, J. M. R. (1998). The effect of particle form on sieve analysis: a test by image analysis. Engineering Geology 50:111-124 DOI: 10.1016/S0013- 7952(98)00004-0.
  • Erdogan, S., & Fowler, D. (2005). Determination of aggregate shape properties using x-ray topographic methods and the effect of shape on concrete rheology. İnternational Center for Aggregate Research, ICAR:106-1.
Year 2023, Volume: 7 Issue: 2, 108 - 115, 15.04.2023
https://doi.org/10.31127/tuje.1053462

Abstract

References

  • Mora, C., Kwan, A., Chan, H. (1998). Particle Size Distribution Analysis of Coarse Aggregate Using Digital Image Processing. Cement and Concrete Research, 28 (6), 921-932.
  • Sezer, A. (2008). Determination of Microapical Properties of Different Types of Soils Using Image Processing Techniques, PhD Thesis, İzmir: Ege University Graduate School of Sciences, Department of Civil Engineering (in Turkish).
  • Murthy, V. N. S. (2002). Geotechnical Engineering, Principles and Practices of Soil Mechanics and Foundation Engineering, CRC Press.
  • Obaidat, M. T., Al-Masaeid, H. R., Gharaybeh, F., & Khedaywi, T. S. (1998). An Innovative Digital Image Analysis Approach to Quantify the Percentage of Voids in Mineral Aggreagates of Bituminous Mixtures. Canadian Journal of Civil Engineering, 25:1041-1049.
  • Gaydecki, P., Silva, I., Fernandes, B. T., Yu, Z. Z. (2000). A Portable Inductive Scanning System for Imaging Steel-Reinforcing Bars Embedded Within Concrete. Sensors and Actuators, 84:25-32.
  • Soroushian, P., Elzafraney, M., Nossoni, A. (2003). Specimen preparation and image processing and analysis techniques for automated quantification of concrete microcracks and voids. Cement and Concrete Research, 33:1949-1962.
  • Soroushian, P., Elzafraney. M. (2005). Morphological operations, planar mathematical formulations, and stereological interpretations for automated image analysis of concrete microstructure. Cement & Concrete Composites, 27:823-833.
  • Felekoglu, B., Güllü, D. (2006). Use of optical microscope and image processing techniques in clinker analysis. IMO Technical Journal, 247, 3761-3770.
  • Sinha, S. K., & Fieguth, P. W. (2006). Automated detection of cracks in buried concrete pipe images. Automation in Construction, 15, 58-72.
  • Park, S. H., Kim, H. K., Morales, A., & Ko, S. J. (2007). Air-void analysis system of polished concrete using image processing. Journal of Applied Computer Science, 15 (2), 19-25.
  • Ozen, M. (2007). Investigation of Relationship Between Aggregate Shape Parameters and Concrete Strength Using Imaging Techniques, Master's Thesis, Ankara: Middle East Technical University, The Graduate School of Natural and Applied Sciences.
  • Comak, B., Beycioğlu, A., Başyigit, C., Kılınçarslan, S. (2011). Use of Image Processing Techniques in Concrete Technology. 6th International Advanced Technologies Symposium (IATS'11), Elazığ.
  • Dipova, N. (2017). Determining The Grain Size Distribution of Granular Soils Using Image Analysis. Acta Geotechnica Slovenica, 1, 29-37.
  • German, R. M. (1994). Powder Metallurgy Science, New Jersey: Metal Powder Industries Federation.
  • Yue, Z. Q., Bekking, W., Morin, I. (1995). Application of digital image processing to quantitative study of asphalt concrete microstructure. Transportation Research Record, 1492:53-60.
  • NG, T. T. (1999). Fabric Study of Granular Materials After Compaction. ASCE Journal of Engineering Mechanics, 125 (12), 1390-1394.
  • Masad, E., & Button, J. (2000). Unified Imaging Approach for Measuring Aggregate Angularity and Texture. Computer-Aided Civil and Infrastructure Engineering, 15(4), 273-280.
  • Mora, C. F., & Kwan, A. K. H. (2000). Sphericity, shape factor, and convexity measurement of coarse aggregate for concrete using digital image processing. Cement and concrete research, 30(3), 351-358.
  • Yue, Z. Q., Chen, S., & Tham, L. G. (2003). Finite element modeling of geomaterials using digital image processing, Computers and Geotechnics, 30, 375-397.
  • Rousan, T. M. A. (2004). Characterization of Aggregate Shape Properties Using a Computer Automated System, Phd Thesis, USA: Texas A&M University.
  • Alshibli, K., & Alsaleh, M. (2004). Characterizing surface roughness and shape of sands using digital microscopy. ASCE Journal of Computing in Civil Engineering, 18 (1), 36-45.
  • Yang, X. (2005). Three Dimensional Characterization of Inheret and Induced Sand Microstructure, Phd Thesis, Georgia Institute of Technology.
  • Hu, R., Yue, Z., Tham, L., & Wang, L. (2005). Digital image analysis of dynamic compaction effects on clay fills. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 131 (11), 1411-1422.
  • Edizer, E. (2006). Particle Size Distribution with Digital Image Processing Method, Master Thesis, Adana: Çukurova University, Institute of Science and Technology.
  • Vangla, P., Roy, N., Mendu, K., & Latha, G. M. (2014). Digital Image Analysis for the Determination of Size and Shape Parameters of Sand Grains. Golden Jubilee Conference of the IGS Bangalore Chapter, Geo Innovations, Bangalore India.
  • Ehsan, K. M., Hossain, M. R., Manzur, T., Shohag, A., & Tabassum, N. (2016). Particle Size Analysis by Image Processing Technique. 1st Bangladesh Civil Engineering Summit, Dhaka, Bangladesh.
  • Jahne, B. (2005). Digital Image Processing, Germany: Springer.
  • Gonzales, R. C., & Woods, R. E. (2001). Digital Image Processing, USA: Prentice Hall.
  • McAndrew, A. (2004). An Introduction to Digital Image Processing with MATLAB, Melbourne: Victoria University of Technology.
  • Gonzales, R. C., & Woods, R. E. (2008). Digital Image Processing, Upper Saddle River, NJ: Pearson Prentice Hall, 1-3.
  • Turkish Standards Institute TS 3530 EN 933-1/April (1999). Experiments for Geometric Properties of Aggregates Part 1: Determination of Particle Size Distribution-Sieving Method
  • Mertens, G, & Elsen, J. (2006). Use of computer assisted image analysis for the determination of the grain- size distribution of sands used in mortars. Cement and Concrete Research 36:1453-1459 DOI: 10.1016/j.cemconres.2006.03.004.
  • Fernlund, J. M. R., Zimmerman, R. W., & Kragic, D. (2007). Influence of volume/mass on grain-size curves and conversion of image-analysis size to sieve size. Engineering Geology, 90:124–137 DOI: 10.1016/j.enggeo.2006.12.007.
  • Allen, T. (1968). Particle size measurement, Chapman and Hall, London.
  • Pettijohn, F. J. (1984). Sedimentary Rocks. S.K. Jain, India.
  • Fernlund, J. M. R. (1998). The effect of particle form on sieve analysis: a test by image analysis. Engineering Geology 50:111-124 DOI: 10.1016/S0013- 7952(98)00004-0.
  • Erdogan, S., & Fowler, D. (2005). Determination of aggregate shape properties using x-ray topographic methods and the effect of shape on concrete rheology. İnternational Center for Aggregate Research, ICAR:106-1.
There are 37 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mert Aydın 0000-0003-0620-0954

T. Fikret Kurnaz 0000-0003-2079-8315

Publication Date April 15, 2023
Published in Issue Year 2023 Volume: 7 Issue: 2

Cite

APA Aydın, M., & Kurnaz, T. F. (2023). An alternative method for the particle size distribution: Image processing. Turkish Journal of Engineering, 7(2), 108-115. https://doi.org/10.31127/tuje.1053462
AMA Aydın M, Kurnaz TF. An alternative method for the particle size distribution: Image processing. TUJE. April 2023;7(2):108-115. doi:10.31127/tuje.1053462
Chicago Aydın, Mert, and T. Fikret Kurnaz. “An Alternative Method for the Particle Size Distribution: Image Processing”. Turkish Journal of Engineering 7, no. 2 (April 2023): 108-15. https://doi.org/10.31127/tuje.1053462.
EndNote Aydın M, Kurnaz TF (April 1, 2023) An alternative method for the particle size distribution: Image processing. Turkish Journal of Engineering 7 2 108–115.
IEEE M. Aydın and T. F. Kurnaz, “An alternative method for the particle size distribution: Image processing”, TUJE, vol. 7, no. 2, pp. 108–115, 2023, doi: 10.31127/tuje.1053462.
ISNAD Aydın, Mert - Kurnaz, T. Fikret. “An Alternative Method for the Particle Size Distribution: Image Processing”. Turkish Journal of Engineering 7/2 (April 2023), 108-115. https://doi.org/10.31127/tuje.1053462.
JAMA Aydın M, Kurnaz TF. An alternative method for the particle size distribution: Image processing. TUJE. 2023;7:108–115.
MLA Aydın, Mert and T. Fikret Kurnaz. “An Alternative Method for the Particle Size Distribution: Image Processing”. Turkish Journal of Engineering, vol. 7, no. 2, 2023, pp. 108-15, doi:10.31127/tuje.1053462.
Vancouver Aydın M, Kurnaz TF. An alternative method for the particle size distribution: Image processing. TUJE. 2023;7(2):108-15.
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