Evaluation of deep learning against conventional limit equilibrium methods for slope stability analysis

Authors

Behnam Azmoon, Michigan Technological UniversityFollow
Aynaz Biniyaz, Michigan Technological UniversityFollow
Zhen (Leo) Liu, Michigan Technological UniversityFollow

Document Type

Article

Publication Date

6-29-2021

Department

Department of Civil, Environmental, and Geospatial Engineering

Abstract

This paper presents a comparison study between methods of deep learning as a new cat-egory of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to cal-culate the factor of safety (FS) using four limit equilibrium methods: Bishop’s simplified method, the Fellenius method, Janbu’s simplified method, and Janbu’s corrected method. The code was ver-ified against Slide2 in RocScience. Subsequently, the average FS values were used to approximate the “true” FS of the slopes for labeling the images for deep learning. Using this code, a comprehensive dataset of slope images with wide ranges of geometries and soil properties was created. The average FS values were used to label the images for implementing two deep learning models: a multiclass classification and a regression model. After training, the deep learning models were used to predict the FS of an independent set of slope images. Finally, the performance of the models was compared to that of the conventional methods. This study found that deep learning methods can reach accuracies as high as 99.71% while improving computational efficiency by more than 18 times compared with conventional methods.

Publisher's Statement

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Publisher’s version of record: https://doi.org/10.3390/app11136060

Publication Title

Applied Sciences (Switzerland)

Recommended Citation

Azmoon, B., Biniyaz, A., & Liu, Z. (2021). Evaluation of deep learning against conventional limit equilibrium methods for slope stability analysis. Applied Sciences (Switzerland), 11(13). http://doi.org/10.3390/app11136060
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15107

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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