3:30 PM - 3:45 PM
[SCG47-07] Investigation of Validity for Evaluation Particle numbers on
Characterization of Soil Particle Morphology Using Automated Particle Image Analysis and Computed Simulations.(2)
Keywords:Particle Size, Particle Shape, Image Analysis
[Introduction]
Information of particle morphology on soil sand, such as particle size and shape aspects are well known that to give prediction for various bulk characteristics on mechanics aspect of grounds. Hence, to characterize particle morphology, an Image Analysis (IA) using a manually microscopic technic is commonly used to investigate particle morphology. However, primary challenges of a conventional IA is a issue of near impossible of measuring a statistically significant number of particles. Typically, realistic particle morphology in natural field should has aspects as broadly distribution. According to ISO and JIS statements is suggested to measuring over than tens of thousands of particles. Furthermore, reason of importance to concern of a deviation model is as well as supported from that typical model of particle size distribution should follow with a lognormal distribution.
Recent advances in computer technology have allowed for the development of a new automated particle image analysis (APIA) approach that uses digital imaging technology. This technology is based on allowed acquisition of a calculation of binarized (2D) particle projection images on each individual particle from on over than ten thousand particles within at least few hours. Hence, these approaches are allowed for the calculation of the various morphological parameters using graphical comparison, such as various distribution graph, such as frequency curve and cumulant curve. However, even if recently progress as mentioned above, it is still only limited suggestion for enough particles to determine distribution on realistic particles which has various aspects. Our group has continuously investigated for effective number of particles on IA. Especially, this report will discuss focused on a shape aspect using APIA and a numerical simulation.
[Method]
Milled Silica sand were used as model samples. A sample pulverization was using P-6 (Fritsch Japan Co.,Ltd) . APIA analyses were conducted on a Morphologi G3SE as automated image analysis system (Malvern 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]
Regarding size distributions, determined any statistical numbers from over than 10,000 particle images were calculated by distribution graph and mean analysis. As primary choosing for shape investigation was set an aspect ratio. It was compared that statistical specific numbers from experiment and simulated results based on several models.
Information of particle morphology on soil sand, such as particle size and shape aspects are well known that to give prediction for various bulk characteristics on mechanics aspect of grounds. Hence, to characterize particle morphology, an Image Analysis (IA) using a manually microscopic technic is commonly used to investigate particle morphology. However, primary challenges of a conventional IA is a issue of near impossible of measuring a statistically significant number of particles. Typically, realistic particle morphology in natural field should has aspects as broadly distribution. According to ISO and JIS statements is suggested to measuring over than tens of thousands of particles. Furthermore, reason of importance to concern of a deviation model is as well as supported from that typical model of particle size distribution should follow with a lognormal distribution.
Recent advances in computer technology have allowed for the development of a new automated particle image analysis (APIA) approach that uses digital imaging technology. This technology is based on allowed acquisition of a calculation of binarized (2D) particle projection images on each individual particle from on over than ten thousand particles within at least few hours. Hence, these approaches are allowed for the calculation of the various morphological parameters using graphical comparison, such as various distribution graph, such as frequency curve and cumulant curve. However, even if recently progress as mentioned above, it is still only limited suggestion for enough particles to determine distribution on realistic particles which has various aspects. Our group has continuously investigated for effective number of particles on IA. Especially, this report will discuss focused on a shape aspect using APIA and a numerical simulation.
[Method]
Milled Silica sand were used as model samples. A sample pulverization was using P-6 (Fritsch Japan Co.,Ltd) . APIA analyses were conducted on a Morphologi G3SE as automated image analysis system (Malvern 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]
Regarding size distributions, determined any statistical numbers from over than 10,000 particle images were calculated by distribution graph and mean analysis. As primary choosing for shape investigation was set an aspect ratio. It was compared that statistical specific numbers from experiment and simulated results based on several models.