Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning

Landslides account for approximately 5% of natural disasters resulting in significant socio-economic impacts. As a major infrastructure issue, slope stability has been traditionally analyzed with multiple deterministic and probabilistic methods to evaluate the stability of slopes or the p...

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Autores principales:	Behnam Azmoon, Aynaz Biniyaz, Zhen Liu, Ye Sun
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Convolutional neural networks deep learning landslides natural hazards resilience slope stability analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/fcba3cc657c74c46bca9bfd8c545c7b1
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id	oai:doaj.org-article:fcba3cc657c74c46bca9bfd8c545c7b1
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spelling	oai:doaj.org-article:fcba3cc657c74c46bca9bfd8c545c7b12021-11-18T00:06:56ZImage-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning2169-353610.1109/ACCESS.2021.3123501https://doaj.org/article/fcba3cc657c74c46bca9bfd8c545c7b12021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9590569/https://doaj.org/toc/2169-3536Landslides account for approximately 5% of natural disasters resulting in significant socio-economic impacts. As a major infrastructure issue, slope stability has been traditionally analyzed with multiple deterministic and probabilistic methods to evaluate the stability of slopes or the probability of landslides. Geotechnical engineers tend to visit the sites of slopes, measure the geometry and soil properties, and use those traditional methods to analyze the slope stability and provide a factor of safety evaluation and recommendation. The fast-growing new technologies such as the internet of things and big data analytics provide new directions for natural hazard prevention. This study is the first to use deep learning as a new method for slope stability analysis for landslide prevention. A convolutional neural network was used to establish the model via transfer learning for processing simulated slope images. After training, our model can accurately predict the factor of safety of slopes for new slope images. Our proposed method was validated by comparing it with a classic limit equilibrium method, i.e., the simplified Bishop method, which is widely used in commercial programs for slope stability analysis. The comparison results showed that our proposed deep learning method outperformed the traditional method by decreasing the computation time by orders of magnitude without sacrificing accuracy. The results demonstrated the possibility and advantages of using deep learning as a new type of slope stability analysis method, including its ability to analyze raw image data directly, high level of automation, satisfactory accuracy, and short computing time, which will enable onsite evaluation for slope stability analysis. Thus, it facilitates fast in-situ decision-making for geotechnical applications and ensures the feasibility of using the internet of things and big data analytics for natural hazard prevention.Behnam AzmoonAynaz BiniyazZhen LiuYe SunIEEEarticleConvolutional neural networksdeep learninglandslidesnatural hazardsresilienceslope stability analysisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150623-150636 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Convolutional neural networks deep learning landslides natural hazards resilience slope stability analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Convolutional neural networks deep learning landslides natural hazards resilience slope stability analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Behnam Azmoon Aynaz Biniyaz Zhen Liu Ye Sun Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning
description	Landslides account for approximately 5% of natural disasters resulting in significant socio-economic impacts. As a major infrastructure issue, slope stability has been traditionally analyzed with multiple deterministic and probabilistic methods to evaluate the stability of slopes or the probability of landslides. Geotechnical engineers tend to visit the sites of slopes, measure the geometry and soil properties, and use those traditional methods to analyze the slope stability and provide a factor of safety evaluation and recommendation. The fast-growing new technologies such as the internet of things and big data analytics provide new directions for natural hazard prevention. This study is the first to use deep learning as a new method for slope stability analysis for landslide prevention. A convolutional neural network was used to establish the model via transfer learning for processing simulated slope images. After training, our model can accurately predict the factor of safety of slopes for new slope images. Our proposed method was validated by comparing it with a classic limit equilibrium method, i.e., the simplified Bishop method, which is widely used in commercial programs for slope stability analysis. The comparison results showed that our proposed deep learning method outperformed the traditional method by decreasing the computation time by orders of magnitude without sacrificing accuracy. The results demonstrated the possibility and advantages of using deep learning as a new type of slope stability analysis method, including its ability to analyze raw image data directly, high level of automation, satisfactory accuracy, and short computing time, which will enable onsite evaluation for slope stability analysis. Thus, it facilitates fast in-situ decision-making for geotechnical applications and ensures the feasibility of using the internet of things and big data analytics for natural hazard prevention.
format	article
author	Behnam Azmoon Aynaz Biniyaz Zhen Liu Ye Sun
author_facet	Behnam Azmoon Aynaz Biniyaz Zhen Liu Ye Sun
author_sort	Behnam Azmoon
title	Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning
title_short	Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning
title_full	Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning
title_fullStr	Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning
title_full_unstemmed	Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning
title_sort	image-data-driven slope stability analysis for preventing landslides using deep learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/fcba3cc657c74c46bca9bfd8c545c7b1
work_keys_str_mv	AT behnamazmoon imagedatadrivenslopestabilityanalysisforpreventinglandslidesusingdeeplearning AT aynazbiniyaz imagedatadrivenslopestabilityanalysisforpreventinglandslidesusingdeeplearning AT zhenliu imagedatadrivenslopestabilityanalysisforpreventinglandslidesusingdeeplearning AT yesun imagedatadrivenslopestabilityanalysisforpreventinglandslidesusingdeeplearning
_version_	1718425264016326656

Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning

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