Compression displacement, aggregate movements and expulsion of water from the soil pores, cause settlement in mass soil, this process called soil consolidation. Because of low permeability of fine grade soils such as clayey soil, consolidation is a time- dependent process that takes place in a long period of time, so, consolidation settlement in saturated fine grade soil has a great importance.
In studding of one dimensional consolidation don by odometer apparatus, by applying static load to a saturated(or nearly saturated) clayey soil, the amount of excess pore water pressure in soil specimen changed and finally transfers from water to soil particles as effective pressure and settlement off soil takes place.
Studding the settlement behavior of soil is one of the first goals in geotechnique engineering and a major problem in design of safe structure that is to be constructed.
In order to develop field conditions, the consolidation test is performed in Casagrande odometer in which settlement of soil samples is noted at different time intervals. This test is performed in according with IS; 2720(part xv) − 1965.
Although rational Casagrande odometer is suitable for determining the consolidation specification of homogenous fine- graded soil, but it is not suitable for coarse aggregate soils because of scale factor (Maryam et al., 2015).
Investigations show that sample size has a strong effect on deformation characteristics of homogenous fine- graded soil, such as Compression Index, Swelling index and coefficient of consolidation (Kongkitkul et al., 2014).
During the loading and unloading process in one dimensional consolidation, results of vertical deformations of soil sample will read by analog gauge or LVDT sensors installed on top of the specimen, in different times. Actually it may be said that this method is a contact method that in addition of spending high cost to preparation of tools, because of human errors, the reliability of the results may be affected.
Nowadays we can see that great development has been made in digital image techniques in many of engineering field, such as geotechnical engineering and many researchers have used digital image to investigation of soil deformation characteristics. Tabrizi- Zarringhabaei et al. (2019) analyzed the granulation curve of fine-grained soils with the help of dynamic image processing technique and compared the results with the traditional method. By analyzing the digital images taken from the solid particles of fine-grained soil with a plasticity index of 30, they analyzed the particle size distribution of the sample and compared the results with the analysis of the hydrometric method and showed that this method can be used for all types of fine-grained soils much more easily than the hydrometric method. Dipova, N., (2017) with the help of digital image processing technique, analyzed the soil granulation curve and by comparing the results with the sieve method, observed that both methods have the same results. T Prabaharan et al. (2020) studied the applications of image processing in different fields. In their research, they focused on the applications of image processing techniques in different fields such as construction, fluid flow, thermal imaging, medical industries, fruit and vegetable industries, and showed that the use of image processing techniques leads to quality products that can reduce time and costs. Snehal, N. Chaudhary. (2016) determined the shape characteristics of aggregates such as elongation, flakiness and sieve analysis with the help of image processing and compared the results with other conventional methods. He showed that the image processing method can provide rapid and accurate method with minimum interference from the operator to determine the shape characteristics of aggregates. Dos Santo set al. (2016) showed that the images from common digital cameras and adequate processing can be used to estimate the moisture content of different soils. Kim et al. (2013) used digital image analysis to evaluate the consolidation behavior of soils under the conditions of radial drainage and to measure the consolidation deformation of the sample. In their research work, Dolrerdeet al. (2014) used digital image processing technique to measure and calculate the volume change of the samples in the three-dimensional test. Amir Hassan et al. (2017) investigated the effects of internal erosion of discrete-grained soil samples on deformation behavior and stress-strain relationship with the help of image processing technique. Erica Ellice et al. (2016) used image processing technique to determine the displacement and volume change of soil samples in three-dimensional devices and developed a non-contact measurement system with the help of image processing. In their study, Elena Kapogiani et al. (2017) used digital imaging and image processing to determine strains and failure mechanisms in slope models during different loadings. Shao Longtanet al. (2013) used the image processing technique in their research work "digital image processing application in the three-dimensional soil dynamic test" to measure the dynamic deformation of the samples. Popescuet al. (2021) based on the processing of images taken from the in situ rock mass; they presented a new method to determine the resistance index (GSI) of the rock mass and showed that this method can be easily used in engineering activities for underground and surface mining. Karisiddappa et al. (2010) developed the application of image processing in the field of engineering. In this research work they adopted digital image processing to extract image feature and laboratory test to estimate the physical properties of same sample then the correlation between image feature and physical soil features is developed. Eichhorn et al. (2020) introduced a low-cost system consisting of 4 computers and four related cameras to measure soil displacement and strain and showed the efficiency of the system by conducting centrifuge modeling of slope failure test. Ajata Sachanet al. (2007) by conducting a series of three axial tests on cylindrical samples of clay soils and using digital image analysis, they investigated the initiation and expansion of strain concentration inside the clay sample caused by the non-uniformity of the soil mass and the hardness of the material.
As mentioned above, the digital image processing technique has been widely used in various fields of engineering, especially in the determination of deformations. However, none of them have focused on determining the soil consolidation coefficient. Image processing technique has ability to determine the deformation without any direct contact with the target of measurement (Shao Longtan, 2013).In this research, image processing technique has been used to determine the consolidation coefficient of clay soils and its efficiency has been shown.
Results show that, digital image processing technique can be used as a suitable and powerful tools to determine deformation characteristics of soil and hence analysis of soil settlement.
1.1 Consolidation coefficient CV
\({C_v}=\frac{{0.197H_{d}^{2}}}{{{t_{50}}}}\) \({C_v}=\frac{{0.197H_{d}^{2}}}{{{t_{50}}}}\) After the consolidation test using one-dimensional consolidation devices, the results related to the deformation of the sample at different times and under the effect of applied loading are obtained. Various methods have been presented by researchers to determine the consolidation coefficient, among these methods; we can mention the time logarithm method (Casagrande 1940) and the time square root method (Taylor 1948). In this research, the time logarithm method was used to determine the soil consolidation coefficient. Based on this method, using the consolidation test results, the settlement-time logarithm curve is drawn and the time related to 50% consolidation is determined, then the consolidation coefficient is determined from the following equation:
Which: \(H_{d}^{2}\) the longest drainage route and \({t_{50}}\) is the time related to 50% consolidation.
1.2 MATLAB software and image processing
MATLAB is a programming platform designed specifically for engineers and scientists to analyze and design systems and products that transform our world. The heart of MATLAB is the MATLAB language, a matrix-based language allowing the most natural expression of computational mathematics. One of the reasons that MATLAB has become such an important tool is through the use of sets of MATLAB programs designed to support a particular task. These sets of programs are called toolboxes, and the particular toolbox of interest to us is the image processing toolbox (Snehal N. Chaudhari, 2016).
1.3 Image processing and its benefit in geotechnical engineering
Image processing is the technique of converting an image into a digital format and performing operations on it to obtain an advanced image or extract some useful information from it. The changes that take place in the images are usually done automatically, relying on carefully designed algorithms. Image processing is a multidisciplinary field, drawing on the help of various branches of science, including mathematics, physics, optics, and electrical engineering. Image processing also overlaps with other areas such as pattern recognition, machine learning, artificial intelligence, and human vision research. The various steps involved in image processing include inputting the image through an optical scanner or digital camera, analyzing and working on the image (data compression, image enhancement, and filtering), and producing the desired output image. The need to extract information from images and interpret their content has been an effective factor in the development of image processing.
In the physical modeling of geotechnical phenomena, in order to observe soil behavior and simulate their real structure, deformations must be measured. The smallness of deformations on the one hand and the small scale of geotechnical models on the other hand have made it difficult to measure these deformations. Due to practical limitations and small number of measurement points, conventional methods cannot fully show the deformation pattern inside the soil. Using image processing technique can solve these limitations. With the help of image processing, displacements can be measured in any position and at any time.
In the normal consolidation test, which is a contact method, apart from high cost to prepare measuring instruments, conducting the test requires spending a long time and also due to the presence of human errors, the reliability of the results is affected. In addition, it is not possible to measure soil deformation at a specific point or surface, and it is not possible to determine the complete pattern of deformation. Therefore, in this research, in order to solve these limitations and access complete information about the deformation behavior of the consolidation sample, image processing technique has been used. Figure 1 shoes the image processing algorithm that used in this research.