Methodology for Resloping of Rock Slope Using 3D Models from UAV-CRP Technology
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 147, Issue 9
Abstract
Assessing the stability of rock slopes is essential to ensuring the typical performance of adjacent transportation infrastructures but simulating the existing field topography and boundary conditions for stability analysis is challenging. This paper describes a case study in which highly weathered rock slopes adjacent to a railroad track in Texas were analyzed using close-range photogrammetry data obtained from an unmanned aerial vehicle with close-range photogrammetry (UAV-CRP). A highly weathered rock mass with low intact strength was considered as a single continuum that undergoes circular failure and was analyzed using Spencer’s two-dimensional (2D) and three-dimensional (3D) limit equilibrium method (LEM) analyses. A 3D critical slip surface was obtained to identify the critical sections on which to conduct the individual 2D stability analysis and obtain a safe resloping angle. These angles were further used to reslope the whole rock slopes and conduct a 3D analysis to arrive at a safe and economic slope angle for each side of the rock-cut. The realistic geometrical slopes developed from the aerial imagery were used to develop a framework for comprehensively analyzing the stability of a rock slope undergoing circular failure.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would also like to thank the Texas Department of Transportation (TxDOT) for granting the funds and support personnel for research project 0-6944. The authors gratefully acknowledge the support and generosity of the NSF Industry-University Cooperative Research Center (I/UCRC) program funded “Center for Integration of Composites into Infrastructure (CICI) site” at TAMU (NSF PD: Dr. Prakash Balan), for its partial support toward this work. The authors would like to thank Cody Lundberg, Kevin Wienhold, and others for their help during data collection. The authors would like to thank Rocscience Inc. for their support.
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Received: Sep 11, 2020
Accepted: Apr 19, 2021
Published online: Jun 18, 2021
Published in print: Sep 1, 2021
Discussion open until: Nov 18, 2021
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