Prediction of mine slope deformation based on PSO-DSRVM

Yusi YUAN; Xiaopeng FENG; Yong LI; Cancan YI

doi:10.16031/j.cnki.issn.1003-8035.202112032

Volume 34 Issue 1

Feb. 2023

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The Chinese Journal of Geological Hazard and Control > 2023 > 34(1): 1-7. > DOI: 10.16031/j.cnki.issn.1003-8035.202112032

YUAN Yusi，FENG Xiaopeng，LI Yong，et al. Prediction of mine slope deformation based on PSO-DSRVM[J]. The Chinese Journal of Geological Hazard and Control，2023，34(1): 1-7. DOI: 10.16031/j.cnki.issn.1003-8035.202112032

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Prediction of mine slope deformation based on PSO-DSRVM

1.
China Railway Wuhan Electrification Bureau Group Co.Ltd.,Wuhan, Hubei　430074, China
2.
Wuhan University of Science and Technology, Wuhan, Hubei　430081, China

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Received Date: December 26, 2021
Revised Date: April 17, 2022
Available Online: November 06, 2022

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Abstract

Abstract

In order to establish a high-precision prediction model of mine slope displacement, Doubly Sparse Relevance Vector Machine （DSRVM） based on Particle Swarm Optimization （PSO） was used to establish the nonlinear relationship between slope stability and influencing factors in this paper. DSRVM was a multi-core combinatorial optimization method, which was proposed under the framework of variational and Relevance Vector Machines （RVM）. Compared with RVM and other multiple-kernel learning methods, DSRVM not only had less training time, but also can obtained higher prediction accuracy. Aiming at the influence of the parameter’s selection of DSRVM on the final prediction effect, the optimal multiple kernel parameters was determined by PSO algorithm to be used in the mine slope displacement prediction. Compared the computational results of DSRVM with Extreme Learning Machine （ELM） and Wavelet Neural Network （WNN）, the feasibility of PSO-DSRVM in slope deformation prediction was verified by the evaluation indicators such as RMSE, R² and ARPE.
- slope stability,
- displacement prediction,
- particle swarm optimization algorithm,
- doubly sparse relevance vector machine

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[1]	王鹏飞. 基于GM-RBF组合模型的高路堑边坡稳定性预测研究[J]. 建筑结构,2021,51(20):140 − 145. [WANG Pengfei. Study on stability prediction of high cutting slope based on GM-RBF combination model[J]. Building Structure,2021,51(20):140 − 145. (in Chinese with English abstract) DOI: 10.19701/j.jzjg.2021.20.023
[2]	方然可,刘艳辉,苏永超,等. 基于逻辑回归的四川青川县区域滑坡灾害预警模型[J]. 水文地质工程地质,2021,48(1):181 − 187. [FANG Ranke,LIU Yanhui,SU Yongchao,et al. A early warning model of regional landslide in Qingchuan County,Sichuan Province based on logistic regression[J]. Hydrogeology & Engineering Geology,2021,48(1):181 − 187. (in Chinese with English abstract) DOI: 10.16030/j.cnki.issn.1000-3665.201911034
[3]	史笑凡,杨春风. 基于支持向量机的边坡垂直位移方向率预测及边坡稳定性研究[J]. 河北工业大学学报,2021,50(1):92 − 98. [SHI Xiaofan,YANG Chunfeng. Support vector machine for vertical displacement direction rate of slope estimate and slope stability research[J]. Journal of Hebei University of Technology,2021,50(1):92 − 98. (in Chinese with English abstract)
[4]	晏红波,杨庆,任超,等. 基于EEMD的BP神经网络边坡预测研究[J]. 水力发电,2017,43(7):37 − 40. [YAN Hongbo,YANG Qing,REN Chao,et al. Research on side slope prediction using BP neural network based on EEMD[J]. Water Power,2017,43(7):37 − 40. (in Chinese with English abstract) DOI: 10.3969/j.issn.0559-9342.2017.07.010
[5]	邓超,胡焕校,张天乐,等. 基于改进极限学习机模型的岩质边坡稳定性评价与参数反演[J]. 中国地质灾害与防治学报,2020,31(3):1 − 10. [DENG Chao,HU Huanxiao,ZHANG Tianle,et al. Stability evaluation and parameter inversion of rock slope using modified extreme learning machine model[J]. The Chinese Journal of Geological Hazard and Control,2020,31(3):1 − 10. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2020.03.01
[6]	韩连生,宋光仁. 基于灰色理论的露天矿边坡位移预测预警研究[J]. 化工矿物与加工,2018,47(5):48 − 52. [HAN Liansheng,SONG Guangren. Research of prediction and early warning of slope displacement in open-pit mines based on Gray Theory[J]. Industrial Minerals & Processing,2018,47(5):48 − 52. (in Chinese with English abstract) DOI: 10.16283/j.cnki.hgkwyjg.2018.05.012
[7]	李麟玮,吴益平,苗发盛,等. 基于变分模态分解与GWO-MIC-SVR模型的滑坡位移预测研究[J]. 岩石力学与工程学报,2018,37(6):1395 − 1406. [LI Linwei,WU Yiping,MIAO Fasheng,et al. Displacement prediction of landslides based on variational mode decomposition and GWO-MIC-SVR model[J]. Chinese Journal of Rock Mechanics and Engineering,2018,37(6):1395 − 1406. (in Chinese with English abstract) DOI: 10.13722/j.cnki.jrme.2017.1508
[8]	LIU Z Q,GUO D,LACASSE S,et al. Algorithms for intelligent prediction of landslide displacements[J]. Journal of Zhejiang University-SCIENCE A,2020,21(6):412 − 429. DOI: 10.1631/jzus.A2000005
[9]	罗亦泳,张豪,张立亭. 基于进化相关向量机的边坡安全系数估算[J]. 人民黄河,2016,38(2):103 − 107. [LUO Yiyong,ZHANG Hao,ZHANG Liting. Estimation of slope safety factor based on evolutionary relevance vector machine[J]. Yellow River,2016,38(2):103 − 107. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-1379.2016.02.027
[10]	江婷,沈振中,徐力群,等. 基于支持向量机-小波神经网络的边坡位移时序预测模型[J]. 武汉大学学报(工学版),2017,50(2):174 − 181. [JIANG Ting,SHEN Zhenzhong,XU Liqun,et al. Time series prediction model of slope displacement based on support vector machines-wavelet nerual network[J]. Engineering Journal of Wuhan University,2017,50(2):174 − 181. (in Chinese with English abstract)
[11]	TIPPING M E. Escaping the convex hull with extrapolated vector machines[M]//Advances in Neural Information Processing Systems 14. MIT: The MIT Press, 2002: .
[12]	沈力华,陈吉红,曾志刚,等. 多稀疏回声状态网络预测模型[J]. 控制理论与应用,2018,35(4):421 − 428. [SHEN Lihua,CHEN Jihong,ZENG Zhigang,et al. Prediction model with multiple sparse echo state network[J]. Control Theory & Applications,2018,35(4):421 − 428. (in Chinese with English abstract) DOI: 10.7641/CTA.2017.70315
[13]	KALTWANG S,TODOROVIC S,PANTIC M. Doubly sparse relevance vector machine for continuous facial behavior estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(9):1748 − 1761. DOI: 10.1109/TPAMI.2015.2501824
[14]	LUO Z Y,BUI X N,NGUYEN H,et al. A novel artificial intelligence technique for analyzing slope stability using PSO-CA model[J]. Engineering with Computers,2021,37(1):533 − 544. DOI: 10.1007/s00366-019-00839-5
[15]	李胜,韩永亮. 基于MFOA-SVR露天矿边坡变形量预测研究[J]. 中国安全生产科学技术,2015,11(1):11 − 16. [LI Sheng,HAN Yongliang. Research on forecasting of slope deformation in open-pit mine based on MFOA-SVR[J]. Journal of Safety Science and Technology,2015,11(1):11 − 16. (in Chinese with English abstract)

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Prediction of mine slope deformation based on PSO-DSRVM

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