UAV photogrammetry for particle size distribution (PSD) and rock fill characterization
Marco Arrieta
In the proceedings of: GeoCalgary 2022: 75th Canadian Geotechnical ConferenceSession: W7
ABSTRACT: The availability of the UAV (unmanned aerial vehicle) technology and the advances in computer image processing has open the door to a new era with several possibilities to determine PSD using aerial photogrammetry, which has been used to great effect in topographical surveys and geological mapping for the last 5 years. It is also an alternative to conventional sieve analysis for PSD estimation. The PSD is essential to determine the mechanical properties, especially shear strength, of the materials (rock fill) stored in waste dumps. Incorrect shear strength and other mechanical parameter estimation are often the root cause of major instabilities. For large waste rock fill the PSD is fundamental to the determine the shear behavior. Traditionally, particle size distribution for coarse grained materials such as rock fill has been obtained through physical sieving. However, the size in hard rock fills can vary significantly and range from smaller particles (<20cm diameter) to blocks or boulders greater than 100cm, with the maximum size usually limited by the in-situ ground conditions and blasting performance. Essentially, the sieving process is impractical to achieve routinely considering the scale of the material and time required. This paper explores the use of UAV photogrammetry to characterize the shear strength in waste dump materials and the influence of PSD on shear strength.
Please include this code when submitting a data update: GEO2022_1010
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Arrieta, Marco (2022) UAV photogrammetry for particle size distribution (PSD) and rock fill characterization in GEO2022. Ottawa, Ontario: Canadian Geotechnical Society.
@article{Arrieta_GEO2022_1010,
author = Marco Arrieta,
title = UAV photogrammetry for particle size distribution (PSD) and rock fill characterization ,
year = 2022
}
title = UAV photogrammetry for particle size distribution (PSD) and rock fill characterization ,
year = 2022
}