Slope stability analysis based on big data and convolutional neural network

doi:10.1007/s11709-022-0859-4

Frontiers of Structural and Civil Engineering

2022, Vol. 16

Issue (7): 882-895 https://doi.org/10.1007/s11709-022-0859-4

本期目录

Slope stability analysis based on big data and convolutional neural network

Yangpan FU, Mansheng LIN, You ZHANG, Gongfa CHEN(

), Yongjian LIU

School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China

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Abstract：

The Limit Equilibrium Method (LEM) is commonly used in traditional slope stability analyses, but it is time-consuming and complicated. Due to its complexity and nonlinearity involved in the evaluation process, it cannot provide a quick stability estimation when facing a large number of slopes. In this case, the convolutional neural network (CNN) provides a better alternative. A CNN model can process data quickly and complete a large amount of data analysis in a specific situation, while it needs a large number of training samples. It is difficult to get enough slope data samples in practical engineering. This study proposes a slope database generation method based on the LEM. Samples were amplified from 40 typical slopes, and a sample database consisting of 20000 slope samples was established. The sample database for slopes covered a wide range of slope geometries and soil layers’ physical and mechanical properties. The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model. The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers, and it can quickly obtain the safety factor and stability state of the slopes. Moreover, the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation. Therefore, the CNN can predict the safety factor of real slopes. In particular, the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN, which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

Key words： slope stability limit equilibrium method convolutional neural network database for slopes big data

收稿日期: 2022-02-25 出版日期: 2022-11-17

Corresponding Author(s): Gongfa CHEN

引用本文:

. [J]. Frontiers of Structural and Civil Engineering, 2022, 16(7): 882-895.
Yangpan FU, Mansheng LIN, You ZHANG, Gongfa CHEN, Yongjian LIU. Slope stability analysis based on big data and convolutional neural network. Front. Struct. Civ. Eng., 2022, 16(7): 882-895.

链接本文:

https://academic.hep.com.cn/fsce/CN/10.1007/s11709-022-0859-4
https://academic.hep.com.cn/fsce/CN/Y2022/V16/I7/882

Fig.1

Tab.1

Fig.2

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Fig.7

slope name	soil layer number	cohesion $c$ (kN/m²)	internal friction angle $φ$ (° )	natural bulk density $γ$ (kN/m³)	saturated unit weight $γ s a t$ (kN/m³)
original slope 1	first layer	15.0 ± R%	12.0 ± R%	20.0 ± R%	15.0 ± R%
	second layer	35.0 ± R%	30.0 ± R%	25.0 ± R%	23.0 ± R%
	third layer	39.0 ± R%	32.0 ± R%	27.0 ± R%	25.0 ± R%
	fourth layer	42.0 ± R%	36.0± R%	35.0 ± R%	30.0 ± R%
original slope 2	first layer	35.0 ± R%	24.0 ± R%	18.0 ± R%	19.0 ± R%
	second layer	35.0 ± R%	32.0 ± R%	24.0 ± R%	25.0 ± R%
	third layer	50.0 ± R%	46.0 ± R%	28.0 ± R%	29.0 ± R%
	fourth layer	54.0 ± R%	50.0 ± R%	34.0 ± R%	35.0 ± R%
original slope 3	first layer	10.0 ± R%	8.0 ± R%	15.0 ± R%	15.0 ± R%
	second layer	20.0 ± R%	8.0 ± R%	20.0 ± R%	20.0 ± R%
	third layer	50 ± R%	20.0 ± R%	25.0 ± R%	25.0 ± R%
	fourth layer	55 ± R%	50.0 ± R%	32.0 ± R%	33.0 ± R%

Tab.2

Fig.8

Fig.9

column	description
1–4	$C, φ, γ, γ s$
5–44	key point coordinates
45–88	second layer data
89–132	third layer data
133–176	fourth layer data

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