Application of digital image processing in the determination of soil consolidation coefficient

Document Type : Research Paper

Authors
1 Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran
2 Departments of Civil Engineering, Ta.C., Islamic Azad University, Tabriz, Iran
3 ‌Departments of Electrical Engineering, Ta.C., Islamic Azad University, Tabriz, Iran
Abstract
In geotechnical engineering, in order to perform analyzes related to consolidation settlement, appropriate values of the consolidation coefficient should be available. In this research, the digital image processing (DIP) technique has been used to determine the deformation of the consolidation sample during the consolidation process. Due to the limitations of the conventional consolidation machine in terms of sample dimensions and also the lack of visibility of the sample, and as a result, the inability to take pictures, a new large-scale consolidation machine has been made. In order to ensure the proper functioning of the new device, 3 series of consolidation tests have been performed on Tabriz saturated clay samples with the new consolidation device as well as the conventional consolidation device. The comparison of the results shows that the consolidation parameters measured by the new device are comparable to the conventional odometer device. Then, by performing 4 series of consolidation tests with a new consolidation device and taking pictures of the sample during the consolidation process, the information related to the deformation of the sample was recorded and with the help of the DIP technique, the deformation of the consolidation sample, and as a result, the load-deformation information was measured and the consolidation coefficient of the samples has been determined based on them. The results show the accuracy and efficiency of the DIP technique in determining soil consolidation parameters. The valuable conclusion of this research is that the image processing technique can be a cost-effective and suitable method for determining the consolidation coefficient, because it does not require measuring devices and eliminates human errors in reading and recording data.
Keywords

[1] S.N. Chaudhari, Analysis of aggregates by Image processing using matlab, Int. J. Innov. Stud. Sci. Engin. Technol. 2 (2016), no. 6, 30-41.
[2] N. Dipova, Determine the grain size distribution of granular soils using image analysis, Acta Geotech. Slovenia 14 (2017), no. 1, 29-37.
[3] G.N. Eichhorn, A. Bowman, S.K. Haigh, and S. Stanier, Low‑cost digital image correlation and strain measurement for geotechnical applications, Strain 56 (2020), no. 6, e12348.
[4] D. Horde, N. Kaikeerati, and P. Jirawattana, Application of image processing for volume measurement in multistage triaxial tests, Adv. Mater. Res. 931 (2014), no. 2, 501-505.
[5] D. Igbinedion and P.H. Simms, Aging and large‑scale consolidation of centrifuge cake oil sands tailings, Conf.: Geovirt. Resil. Innov., Carleton University, Canada, 2020.
[6] E. Kapogianni and M. Sakellariou, Application of particle image velocimetry (PIV) and digital image correlation (DIC) techniques on scaled slope models, Int. Res. J. Engin. Technol. 4 (2017), no. 9, 853-860.
[7] R. Karisiddappa and Sh. Shridhara, Soil characterization based on digital image analysis, Indian Geotech. Conf., GEO trendz, IGS Mumbai Chapter & IIT Bombay, 2010.
[8] J.Y. Kim, C.K. Chung, N.G. Cho, and C.Y. Yune, Evaluation of consolidation behavior of soils under radial drainage condition using digital image analysis, Proc. 18th Int. Conf. Soil Mech. Geotech. Engin., Paris, 2013.
[9] W. Kongkitkul, K. Kongwisawamitr, V. Suwanwattana, V. Thaweeprasart, and R. Sukkarak, Comparison of one dimensional characteristics of clays by using two different specimen sizes, Soil Behav. Geomech. 2014 (2014), 333-342.
[10] Sh. Longtan, Y. Song, S. Yong, H. Chuan, and G. Xiaoxia, Application of digital image processing technology in dynamic triaxial test of soil mechanics, J. Theor. Appl. Inf. Technol. 48 (2013), no. 3, 87-96.
[11] A.H. Mehdizadeh, M.M. Disfani, R. Evans, A. Arulrajah, and D.E.L. Ong, Application of image processing in internal erosion investigation, Proc. 19th Int. Conf. Soil Mech. Geotech. Engin., Seoul, 2017.
[12] M. Mokhtari, N. Shriatmadari, A.A.R. Heshmati, and H. Salehzadeh, Design and fabrication of a large‑scale odometer, J. Cent. South Univ. 22 (2015), no. 1, 931-936.
[13] S. Popescu and O.B. Tomus, Determination of the rock mass resistance index (GSI) based on image processing, MATEC Web Conf. 342, UNIVERSITARIA SIMPRO, 2021.
[14] T. Prabaharan, P. Periasamy, V. Mugendiran, and Ramanan, Studies on application of image processing in various fields: An overview, IOP Conf. Ser. Mater. Sci. Engin. 961 (2020), no. 1, 012006.
[15] A. Sachan and D. Pennumadu, Strain localization in solid cylindrical clay specimen using Digital Image Analysis (DIA) Technique, Soils Found. Japan. Geotech. Soc. 47 (2007), no. 1, 67-78.
[16] E.E. Saloma Uy and T. Boonyatee, Image processing for geotechnical laboratory measurements, Int. J. GEOMATE 10 (2016), no. 22, 1964-1970.
[17] J.F.C.D. Santos, H.R.F. Silva, F.A.C. Pinto, and I.R. de Assis, Use of digital images to estimate soil moisture, Rev. Brasil. Engen. Agrí. Ambien. 20 (2016), no. 12, 1051-1056.
[18] S. Tabrizi‑Zarringhabaei, R. Goli Ejlali, M. Yousefzadeh Fard, and S.J. Sayyed Fattahi, An image‑based method to determine the particle size distribution (PSD) of fine‑grained soil, Mini.‑Geo.‑Petrol. Engin. Bull. 34 (2019), no. 3, 81-88.

Articles in Press, Corrected Proof
Available Online from 06 August 2026

  • Receive Date 10 January 2025
  • Revise Date 17 February 2025
  • Accept Date 19 February 2025