基于Box-Jenkins随机模型的滑坡稳定性预测模型

张泰丽; 吴廷尧; 王鲁琦; 张震

doi:10.3799/dqkx.2023.036

Volume 48 Issue 5

May 2023

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Article Navigation > Earth Science > 2023 > 48(5): 1989-1999

Zhang Taili, Wu Tingyao, Wang Luqi, Zhang Zhen, 2023. Nonlinear Prediction of Landslide Stability Based on Machine Learning. Earth Science, 48(5): 1989-1999. doi: 10.3799/dqkx.2023.036

Citation:

Zhang Taili, Wu Tingyao, Wang Luqi, Zhang Zhen, 2023. Nonlinear Prediction of Landslide Stability Based on Machine Learning. Earth Science, 48(5): 1989-1999. doi: 10.3799/dqkx.2023.036

Zhang Taili, Wu Tingyao, Wang Luqi, Zhang Zhen, 2023. Nonlinear Prediction of Landslide Stability Based on Machine Learning. Earth Science, 48(5): 1989-1999. doi: 10.3799/dqkx.2023.036

Citation:

Zhang Taili, Wu Tingyao, Wang Luqi, Zhang Zhen, 2023. Nonlinear Prediction of Landslide Stability Based on Machine Learning. Earth Science, 48(5): 1989-1999. doi: 10.3799/dqkx.2023.036

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Nonlinear Prediction of Landslide Stability Based on Machine Learning

doi: 10.3799/dqkx.2023.036

Zhang Taili^{1, 2
,
,},
Wu Tingyao^{3, 4
,
,
,},
Wang Luqi⁴,
Zhang Zhen⁵

1.
Nanjing Geological Survey Center, China Geological Survey, Nanjing 210016, China
2.
Key Laboratory of Geological Disaster Prevention and Control in Hills and Mountains, Ministry of Natural Resources, Fuzhou 350002, China
3.
China State Construction Bridge Corporation Limited, Chongqing 402260, China
4.
College of Civil Engineering, Chongqing University, Chongqing 400045, China
5.
State Key Laboratory of Fine Blasting, Jianghan University, Wuhan 430056, China

Received Date: 2022-11-27
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

The prediction and stability analysis of landslide disaster have great engineering significance and application value. Machine learning algorithm is mainly used in landslide displacement prediction, but is limited in landslide stability analysis. Therefore, in order to more accurately analyze the stability of bedding rock slope under cyclic seismic load, the strain softening process of sliding zone soil was obtained by combining the research methods of indoor physical model test and the comparison of discrete element numerical simulation software. In addition, a landslide stability prediction model based on machine learning algorithm is proposed by taking advantage of the nonlinear characteristics of landslide deformation. The results show follows: (1) The gradual reduction of shear stress promotes the strain-softening process of soil in the sliding zone. Although confining pressure of soil in the sliding zone can inhibit the increase of cracks in the sliding zone, its inhibition effect on strain softening is limited. (2) The ARIMA(1, 1, 0)(0, 1, 1) model with the standard BIC value of 8.160 was established to accurately predict the time series data of the slope stability coefficient. Based on the field observation of the slope stability coefficient and stress field, two possible landslide-triggering mechanisms are described. Mechanical learning of time series can accurately predict the variation law of slope stability coefficient under cyclic load.
- machine learning,
- landslide,
- stability calculation,
- time series,
- engineering geology

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References(41)

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