A New Approach for Particle Size Characterization of Soil Based on Dynamic Image Analysis (Dia)

Abstract

Particle size distribution (PSD) affects soil behaviour and plays a main role in geotechnical engineering. In site investigations of large engineering structures, in earthquakes and liquefaction research and before filter material selection, it is necessary to make a number of PSD analyses. There are two popular techniques to determine PSD in the lab: sieving for the coarse-grained samples and hydrometer analysis for fine-grained samples. However, determining the PSD of soil in the lab has some challenges such as time, labour, difficulty and errors related to the analysis. The newly developed dynamic image analysis (DIA) method based on wavelet transformation (WT) analysis is used as the main approach for determining particle size distribution. The DIA method contains saving the image and processing it with the software for a specified goal. The DIA method holds many advantages over the hydrometer analysis, including automating the test, reducing errors, monitoring and evaluating the particles at any time. In this paper, the PSD of fine-grained soil with a plasticity index (PI(=30 has been determined. The images of soil were prepared with a digital camera at a different time when particles settled through a sedimentation cylinder and were analysed with MATLAB software. The results were compared with the hydrometer analysis. The results showed that at t=15 min, a complete overlapping was seen between the DIA method and hydrometer analysis. The D 20 , D 50 and D 80 values by the DIA method were determined respectively, 0.0017, 0.012 and 0.031 mm at t=15 min. The results demonstrated that the DIA technique can be implemented for all types of fine-grained soils, more easily than the hydrometer analysis. The differences between the methods' results were raised due to the amount of data provided by them.

Development of New Correlation Fines Content, NSPT and CPT Using Neural Network Approach and Multilinear Regression to Support Liquefaction Hazard Analysis

“…The analysis can then be estimated by summing the probabilities of all potential sequences of parameters that determine the liquefaction, which involves combination in the liquefaction domain. The grain size parameter taken from the CPT data is important to determine accomplishment in calculating the liquefaction probability (Tabrizi-Zarringhabaei et al, 2019). This study uses the MLR and ANN approaches to obtain the good quality assessment of the FC value from several available geotechnical parameters.…”

Evaluation of the Effect of the Moisture Content of Coal Dust on the Prediction of the Coal Dust Explosion Index

“…Therefore, in order to prevent the explosion of coal dust, adequate studies and investigations of the mechanism of coal dust explosion should be carried out (Ranganathan et al, 2018). The explosion index of coal dust depends on various parameters, including environmental conditions and inherent characteristics of coal dust (Moradi et al, 2019). Moisture content is one of the most important factors in dust explosion studies (Yu et al, 2012).…”