5:15 PM - 6:45 PM
[SMP23-P06] Validity for Evaluation Particle numbers on Characterization of Soil Particle Morphology Using Automated Particle Image Analysis and Computed Simulations.(4)
Keywords:Particle Size, Particle Shape, Image Analysis
[Introduction]
Soil particle morphology, including shape and size, plays a crucial role in predicting various bulk characteristics and understanding the mechanical behavior of the ground at a microscopic insight. To address this, Image Analysis (IA) employing manual microscopic techniques is commonly utilized. However, a primary challenge faced by conventional IA is the difficulty in measuring a statistically significant number of particles. Realistic particle morphology in natural fields typically exhibits a broad distribution, and standards from ISO and JIS recommend the measurement of tens of thousands of particles to ensure accuracy.
Moreover, it is essential to consider a deviation model, suggested by the typical model of particle size distribution should be a lognormal distribution. Recent advancements in computer technology have paved the way for a new automated particle image analysis (APIA) approach utilizing digital imaging technology. This technology enables the acquisition and calculation of binarized (2D) particle projection images for each individual particle from a dataset of over ten thousand particles within a few hours. These advancements allowed for the calculation of various morphological parameters through graphical comparisons, including frequency curves and cumulant curves. However, despite recent progress, there remains a limited determination method for verification a sufficient number of particles to determine realistic distribution across various aspects.
Our research group has consistently focused on addressing this limitation in IA's particle count. This presentation will specifically discuss the shape aspect, leveraging APIA in conjunction with statistical modeling and simulation to enhance our understanding of particle distribution.
[Method]
Milled Silica sand were used as model samples. APIA analyses were conducted on a Morphologi 4 as automated image analysis system (Malvern Panalytical Instruments, Worcestershire, UK). Sample was subsequently dispersed with an SDU using a short duration pulse of compressed air. Measurements were collected automatically using standard operating procedures (SOPs), which clearly define the software and hardware settings used during the measurement process. The measurement sample was dispersed on a glass plate, which was used as a sample carrier to minimize environmental exposure within the enclosed sample chamber unit.
A computer simulation was carried out mainly Scilab platform with implemented own programming to calculate various numerical model, MS Excel used as supportive.
[Simulation Design]
In this study, we adopted a method of comparing the circularity obtained by directly altering the shape using a simulation with the measured value. Specifically, a polar coordinate function is employed to introduce roughness to the end face using random numbers. The number of vertices can be defined computationally. The particle shape distribution and each characteristic value were compared with the frequency of occurrence of shapes as the number of particles. To minimize the influence of particle size, the actual measurement data were verified by constraining the particle size. Furthermore, this report will discuss the mode value number as the peak top of distributions to investigate the relationship with parameters for modeling.
Soil particle morphology, including shape and size, plays a crucial role in predicting various bulk characteristics and understanding the mechanical behavior of the ground at a microscopic insight. To address this, Image Analysis (IA) employing manual microscopic techniques is commonly utilized. However, a primary challenge faced by conventional IA is the difficulty in measuring a statistically significant number of particles. Realistic particle morphology in natural fields typically exhibits a broad distribution, and standards from ISO and JIS recommend the measurement of tens of thousands of particles to ensure accuracy.
Moreover, it is essential to consider a deviation model, suggested by the typical model of particle size distribution should be a lognormal distribution. Recent advancements in computer technology have paved the way for a new automated particle image analysis (APIA) approach utilizing digital imaging technology. This technology enables the acquisition and calculation of binarized (2D) particle projection images for each individual particle from a dataset of over ten thousand particles within a few hours. These advancements allowed for the calculation of various morphological parameters through graphical comparisons, including frequency curves and cumulant curves. However, despite recent progress, there remains a limited determination method for verification a sufficient number of particles to determine realistic distribution across various aspects.
Our research group has consistently focused on addressing this limitation in IA's particle count. This presentation will specifically discuss the shape aspect, leveraging APIA in conjunction with statistical modeling and simulation to enhance our understanding of particle distribution.
[Method]
Milled Silica sand were used as model samples. APIA analyses were conducted on a Morphologi 4 as automated image analysis system (Malvern Panalytical Instruments, Worcestershire, UK). Sample was subsequently dispersed with an SDU using a short duration pulse of compressed air. Measurements were collected automatically using standard operating procedures (SOPs), which clearly define the software and hardware settings used during the measurement process. The measurement sample was dispersed on a glass plate, which was used as a sample carrier to minimize environmental exposure within the enclosed sample chamber unit.
A computer simulation was carried out mainly Scilab platform with implemented own programming to calculate various numerical model, MS Excel used as supportive.
[Simulation Design]
In this study, we adopted a method of comparing the circularity obtained by directly altering the shape using a simulation with the measured value. Specifically, a polar coordinate function is employed to introduce roughness to the end face using random numbers. The number of vertices can be defined computationally. The particle shape distribution and each characteristic value were compared with the frequency of occurrence of shapes as the number of particles. To minimize the influence of particle size, the actual measurement data were verified by constraining the particle size. Furthermore, this report will discuss the mode value number as the peak top of distributions to investigate the relationship with parameters for modeling.