Remote Sensing (Oct 2023)

Drone Photogrammetry for Accurate and Efficient Rock Joint Roughness Assessment on Steep and Inaccessible Slopes

  • Jiamin Song,
  • Shigui Du,
  • Rui Yong,
  • Changshuo Wang,
  • Pengju An

DOI
https://doi.org/10.3390/rs15194880
Journal volume & issue
Vol. 15, no. 19
p. 4880

Abstract

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The roughness of rock joints exerts a substantial influence on the mechanical behavior of rock masses. In order to identify potential failure mechanisms and to design effective protection measures, the accurate measurement of joint roughness is essential. Traditional methods, such as contact profilometry, laser scanning, and close-range photogrammetry, encounter difficulties when assessing steep and inaccessible slopes, thus hindering the safety and precision of data collection. This study aims to assess the feasibility of utilizing drone photogrammetry to quantify the roughness of rock joints on steep and inaccessible slopes. Field experiments were conducted, and the results were compared to those of 3D laser scanning in order to validate the approach’s procedural details, applicability, and measurement accuracy. Under a 3 m image capture distance using drone photogrammetry, the root mean square error of the multiscale model-to-model cloud comparison (M3C2) distance and the average roughness measurement error were less than 0.5 mm and 10%, respectively. The results demonstrate the feasibility and potential of drone photogrammetry for joint roughness measurement challenges, providing a useful tool for practitioners and researchers pursuing innovative solutions for assessing rock joint roughness on precipitous and hazardous slopes.

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