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http://dx.doi.org/10.12814/jkgss.2022.21.2.021

Calculation of Shear Strength of Rock Slope Using Deep Neural Network  

Lee, Ja-Kyung (Dept. of Civil Engineering, Korea Maritime and Ocean Univ.)
Choi, Ju-Sung (Dept. of Civil Engineering, Korea Maritime and Ocean Univ.)
Kim, Tae-Hyung (Dept. of Civil Engineering, Korea Maritime and Ocean Univ.)
Geem, Zong Woo (Dept. of Smart City & Energy, Gachon Univ.)
Publication Information
Journal of the Korean Geosynthetics Society / v.21, no.2, 2022 , pp. 21-30 More about this Journal
Abstract
Shear strength is the most important indicator in the evaluation of rock slope stability. It is generally estimated by comparing the results of existing literature data, back analysis, experiments and etc. There are additional variables related to the state of discontinuity to consider in the shear strength of the rock slope. It is difficult to determine whether these variables exist through drilling, and it is also difficult to find an exact relationship with shear strength. In this study, the data calculated through back analysis were used. The relationship between previously considered variables was applied to deep learning and the possibility for estimating shear strength of rock slope was explored. For comparison, an existing simple linear regression model and a deep learning algorithm, a deep neural network(DNN) model, were used. Although each analysis model derived similar prediction results, the explanatory power of DNN was improved with a small differences.
Keywords
Shear strength; Linear regression; Deep neural network; Explanatory power;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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