Semiautomatic Determination of the Geological Strength Index Using SfM and ANN Techniques
Publication: International Journal of Geomechanics
Volume 21, Issue 12
Abstract
With the popularization of unmanned aerial vehicles, low-cost, high-precision photogrammetry has become a useful tool for mapping and obtaining information in areas that are difficult to access. It has been applied in geomechanics because it makes it possible to obtain information from rock masses for which there is no alternative way to obtain information by traditional mapping methods, increases the frequency of analysis and reduces the exposure of professionals to unsafe conditions. In this study, we used the technique called structure from motion to acquire 2D images and create a three-dimensional point cloud. Then, using artificial neural network techniques, we implemented a semiautomatic classification of the rock mass using the Geological Strength Index (GSI). The routine was validated using five datasets with different geological characteristics, choosing the neural network with the best performance, presenting results with confidence intervals above 90%, 100% hit rates, and a low mean squared error. The procedure allows a standardized interpretation, intended to reduce the bias generated by the interpretation through a rapid and repeatable analysis, in addition to the creation of a systematic record report.
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Data Availability Statement
The code used in this study is available online in accordance with funder data retention policies. The algorithm was written in m-code, and the open source code is available on the GSI_Classifier_v1 GitHub page https://github.com/MariliaAbrao/GSI_Classifier_v1 (Zeni et al. 2020a).
The data used in this study are available online in accordance with funder data retention policies. The data are available on the Figshare repository. https://doi.org/10.6084/m9.figshare.13123061 (Zeni et al. 2020b).
Acknowledgments
The equipment used was supplied by the Mining Engineering Department of the Federal University of Rio Grande do Sul. The work was partially financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), a foundation linked to the Ministry of Education of Brazil.
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International Journal of Geomechanics
Volume 21 • Issue 12 • December 2021
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© 2021 American Society of Civil Engineers.
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Received: Oct 22, 2020
Accepted: Jul 28, 2021
Published online: Oct 14, 2021
Published in print: Dec 1, 2021
Discussion open until: Mar 14, 2022
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