Investigation on the Processing of LiDAR Point Cloud Data for Particle Size Measurement of Aggregates as an Alternative to Image Analysis
Grain size distribution is accepted as the basic control parameter in production processes in fields such as chemistry, mining, metallurgy, materials, construction, as well as in natural processes such as in the field of geology. Depending on the size of the grain, the methods used to determine the grain size are also varied. Laser scanning is a measurement technique in which 3D point information is obtained at very high speed compared to conventional measurement techniques. In our study, limestone aggregate samples were prepared by crushing and sieving into mixtures with different size distributions. Scaled digital photographs of these samples having known grain size distribution were taken, image analysis was applied on them, and the size distributions were determined. After that, the surfaces of the prepared mixtures were scanned using a terrestrial laser scanner and transferred to the computer environment as point cloud data. These data were analyzed by two different methods and first the D80, D50, and D30 characteristic sizes, representing the sieve aperture or the corresponding grain diameter through which 80%, 50%, and 30% of the sample passes, respectively, in the cumulative grain size distribution graph, were measured by roughness analysis method and then the size distribution were determined by analyzing point cloud data with a new algorithm. The validity of the methods was evaluated by comparing the D80, D50, and D30 sizes and size distributions determined by different methods with each other and with the sieve analysis results. According to the results obtained in our study, the analysis of LiDAR point cloud data can be successfully applied to determine the D80, D50, and D30 sizes and size distribution of aggregate piles.
I. C. Engin and N. H. Maerz, "Investigation on the Processing of LiDAR Point Cloud Data for Particle Size Measurement of Aggregates as an Alternative to Image Analysis," Journal of Applied Remote Sensing, vol. 16, no. 1, article no. 16511, Society of Photo-optical Instrumentation Engineers, Feb 2022.
The definitive version is available at https://doi.org/10.1117/1.JRS.16.016511
Geosciences and Geological and Petroleum Engineering
Keywords and Phrases
Algorithm; Image Analysis; LiDAR; Point Cloud; Roughness; Size Distribution; Terrestrial Laser Scanning
International Standard Serial Number (ISSN)
Article - Journal
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26 Feb 2022