Novel Approach for Particle Size Distribution Analysis. Applied Case to Rockfills and Waste Dumps Using Unmanned Aerial Vehicle (UAV)
- DOI
- 10.2991/978-94-6463-104-3_2How to use a DOI?
- Keywords
- Particle Size Distribution (PSD); Unmanned Aerial Vehicle (UAV); Photogrammetry
- Abstract
This paper describes the workflow and results for Particle Size Distribution (PSD) analysis using UAV photogrammetry for waste rockfill materials. A methodology for digital detection and statistical measurement of PSD derived from UAV-SfM photogrammetry is presented. The comparative results between field and digital measurements indicate that the average deviation, and standard deviation between the manually and digitally particle size vary between 12.3 mm and 49.9 mm for the field measurements and 16.9 mm and 52.5 mm for UAV in the different materials. PSD estimated using conventional and image processing shows a 4 mm an average difference between the measurements showing the potential use of the UAV technology and image processing to estimate PSD, leading to implement as standard practice aerial photogrammetry as an alternative to conventional sieve analysis for PSD estimation.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Marco Arrieta PY - 2023 DA - 2023/03/01 TI - Novel Approach for Particle Size Distribution Analysis. Applied Case to Rockfills and Waste Dumps Using Unmanned Aerial Vehicle (UAV) BT - Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022) PB - Atlantis Press SP - 5 EP - 14 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-104-3_2 DO - 10.2991/978-94-6463-104-3_2 ID - Arrieta2023 ER -