Abstract
This paper discusses the PSD effects on the behavior of soil and its properties. The coarse grain part of soil can be analyzed with sieve analysis. Evaluating the PSD of fi ne-grained soil with the traditional method (hydrometer analysis) is a time-consuming process, has laboratory complexities and the obtained results are not accurate. Due to these limitations, there is a demand for a method that can offer faster data with more accuracy for geotechnical engineering. In this paper, the digital image processing (DIP) method is proposed using microscopic images and MATLAB software to determine the PSD of fine-grained soil. The DIP method was compared with the hydrometer analysis. The analysis of the data obtained from these two methods indicated that the image based method is capable of providing faster and more comprehensive results of the PSD.
Highlights
One of the most useful pieces of data about soil is regarding the size of its particles
The results of the digital image processing (DIP) were compared to hydrometer analysis as shown in Figures 9, 10, 11, and 12
The results showed that the DIP method could be used for all type of fine-grained soil and it is more trustworthy than hydrometer analysis
Summary
One of the most useful pieces of data about soil is regarding the size of its particles. The PSD is an important part of soil mechanics, especially geotechnical engineering. The PSD of soil is utilized for soil classification and it can be applied in the forecasting of every mechanical and hydrologic soil behavior containing the specific weight, cohesion, permeability, etc. Hydrometer analysis is based on the principle of sedimentation and Stoke’s law so it has some limitations and it is a time-consuming process (Coates and Hulse, 2012). Digital image processing is used in criminology for fingerprint matching, automatically interpreting fingerprints for dermatoglyphic applications (Mardia et al, 1997). Image processing plays an important role in many medical treatments, for example, magnetic resonance imaging and digital mammography are an important sector of diagnoses (Pham et al, 2000).
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