Deformation Intelligent Prediction Model Based on Gaussian Process Regressionand Application

WANG Jianmin; ZHANG Jin

doi:10.13203/j.whugis20160075

Volume 43 Issue 2

Feb. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(2): 248-254. > DOI: 10.13203/j.whugis20160075

WANG Jianmin, ZHANG Jin. Deformation Intelligent Prediction Model Based on Gaussian Process Regressionand Application[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 248-254. DOI: 10.13203/j.whugis20160075

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Deformation Intelligent Prediction Model Based on Gaussian Process Regressionand Application

WANG Jianmin^1,,
ZHANG Jin¹

1.
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Funds:

The Natural Science Foundation of Shanxi Province 201701D121014

the National Natural Science Foundation of China 41371373

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Author Bio:
WANG Jianmin, associate professor, specializes in deformation monitoring. E-mail: 8844.4321@163.com
Received Date: May 17, 2016
Published Date: February 04, 2018

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Abstract

Abstract

The deformation of building structures or rock mass usually has the features of complexity and nonlinearity that the general regression model cannot accurately predict. In this paper, gaussian process regression(GPR) theory is applied in time series analysis of nonlinear deformation monitoring data. Considering the unceasing updates and massive accumulation of monitoring data, the hyper-parameter and the adaptability of sample set, a "progressive~truncation type" hyper-parameter automatic update mode and selection method for training sample set was developed. On this basis, a GPR time-driven deformation intelligent prediction model(GPR-TIPM) was constructed. This model was applied to the nonlinear time series analysis of monitoring points on a mine slope. By analyzing the deformation trend, a composite kernel optimization method including the "Matérn32" and square exponential covariance kernel function is proposed. The experimental results showed that the prediction performance of the combined kernel function is better than that of the single kernel function, and improved the generalization ability of the model The prediction effect of GPR-TIPM model is better in the short term.
- deformation monitoring,
- gaussian process regression,
- intelligent predict,
- time series,
- mine slope

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References

[1]	Stojanovic B, Milivojevic M, Ivanovic M. Adaptive System for Dam Behavior Modeling Based on linear Regression and Genetic Algorithms[J]. Advances in Engineering Software, 2013, 65:182-190 doi: 10.1016/j.advengsoft.2013.06.019
[2]	卢骏, 戴吾蛟, 章浙涛.大坝变形变系数回归建模[J].武汉大学学报·信息科学版, 2015, 40(1):139-142 http://ch.whu.edu.cn/CN/abstract/abstract3171.shtml Lu Jun, Dai Wujiao, Zhang Zhetao.Modeling Dam Deformatin Using Varying Goefficient Regression[J]. Geomatics and Information Science of Wuhan University, 2015, 40(1):139-142 http://ch.whu.edu.cn/CN/abstract/abstract3171.shtml
[3]	Grelle G, Guadagno I F M. Regression Analysis for Seismic Slope Instability Based on a Double Phase Viscoplastic Sliding Model of the Rigid Block[J]. Landslides, 2013, 10(5):583-597 doi: 10.1007/s10346-012-0350-8
[4]	陈晓鹏, 张强勇, 刘大文.边坡变形统计回归分析模型及应用[J].岩石力学与工程学报, 2008, 27(S2):3673-3678 http://www.doc88.com/p-95325619553.html Chen Xiaopeng, Zhang Qiangyoun, Liu Dawen. Deformation Statistical Regressiong Analysis Model of Slope and Its Application[J]. Chinese Journal of Rock Mechanics and Engineering, 2008, 27(S2):3673-3678 http://www.doc88.com/p-95325619553.html
[5]	Wang Q, Wang C, Xie R. An Improved SCGM(1, m) Model for Multi-point Deformation Analysis[J]. Geosciences Journal, 2014, 18(4):477-484 doi: 10.1007/s12303-014-0012-z
[6]	黄声享, 尹晖, 蒋征.变形监测数据处理[M].武汉:武汉大学出版社, 2012 Huang Shengxiang, Yin Hui, Jiang Zheng.Deformation Monitoring Data Processing[M]. Wuhan:Wuhan University Press, 2010
[7]	武雪玲, 任福, 牛瑞卿.多源数据支持下的三峡库区滑坡灾害空间智能预测[J].武汉大学学报·信息科学版, 2013, 38(8):963-968 http://ch.whu.edu.cn/CN/abstract/abstract2728.shtml Wu Xueling, Ren Fu, Niu Ruiqing. Spatial Intelligent Prediction of Landslide Hazard Based on Multi-source Data in Three Corges Reservoir Area[J]. Geomatics and Information Science of Wuhan University, 2013, 38(8):963-968 http://ch.whu.edu.cn/CN/abstract/abstract2728.shtml
[8]	Li L, Huang G. Procedia Engineering Prediction of Goaf Settlement with Time Sequence of Wavelet Neural Network[J]. Procedia Engineering, 2011, 15:4723-4727 doi: 10.1016/j.proeng.2011.08.884
[9]	Suwansawat S, Einstein H H. Artificial Neural Networks for Predicting the Maximum Surface Settlement Caused by EPB Shield Tunneling[J]. Tunnelling and Underground Space Technology, 2006, 21:133-150 doi: 10.1016/j.tust.2005.06.007
[10]	张正禄, 黄全义, 文鸿雁.工程的变形监测分析与预报[M].北京:测绘出版社, 2007 Zhang Zhenglv, Huang Quanyi, Wen Hongyan. Deformation Monitoring Analysis and Prediction for Engineering Constructions[M]. Beijing:Surveying and Mapping Press, 2007
[11]	Li P, Tan Z, Yan L, et al. Time Series Prediction of Mining Subsidence Based on a SVM[J]. Mining Science and Technology, China University of Mining & Technology, 2011, 21(4):557-562 https://www.sciencedirect.com/science/article/pii/S1674526411000998
[12]	李德江, 花向红, 李涛.基于支持向量机的建筑物沉降预测模型研究[J].测绘工程, 2009, 18(3):29-31 http://industry.wanfangdata.com.cn/yj/Detail/Periodical?id=Periodical_chgc200903008 Li Dejiang, Hua Xianghong, Li Tao.Researchon Predictive Modeling of Bulding Settlement Based on Suport Vector Mechine[J]. Engineering of Surveying and Mapping, 2009, 18(3):29-31 http://industry.wanfangdata.com.cn/yj/Detail/Periodical?id=Periodical_chgc200903008
[13]	Zhang H, Wang Y, Li Y. SVM Model for Estimating the Parameters of the Probability-integral Method of Predicting Mining Subsidence[J]. Mining Science and Technology (China), China University of Mining and Technology, 2009, 19(3):385-388 doi: 10.1016/S1674-5264(09)60072-7
[14]	Li S, Zhao H, Ru Z. Deformation Prediction of Tunnel Surrounding Rock Mass Using CPSO-SVM Model[J]. Journal of Central South University, 2012, 19(11):3311-3319 doi: 10.1007/s11771-012-1409-3
[15]	潘平.基于小波神经网络理论的边坡位移预测[J].成都理工大学学报(自然科学版), 2006, 33(2):176-180 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=cdlg200602009&dbname=CJFD&dbcode=CJFQ Pan Ping.Slope Displacement Forecase Based Onwavelet Neural Network[J]. Journal of Chengdu University of Technology:Sci & Technolgy, 2006, 33(2):176-180 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=cdlg200602009&dbname=CJFD&dbcode=CJFQ
[16]	何志昆, 刘光斌, 赵曦晶.高斯过程回归方法综述[J].控制与决策, 2013, 28(8):1121-1129 http://www.wenkuxiazai.com/doc/fb36bd03d4d8d15abe234eb2.html He Zhikun, Liu Guangbin, Zhao Xijing. Overview of Gaussian Process Regression[J]. Control and Decision, 2013, 28(8):1121-1129 http://www.wenkuxiazai.com/doc/fb36bd03d4d8d15abe234eb2.html
[17]	Su G S. Gaussian Process-based Dynamic Response Surface Method for Estimating Slope Failure Probability[J]. Yantu Lixue/Rock and Soil Mechanics, 2014, 35(12):3592-3601 https://www.sciencedirect.com/science/article/pii/S016747301730214X
[18]	张研. 地下工程岩体非线性行为预测识别的高斯过程模型与动态智能反馈分析[D]. 南宁: 广西大学, 2013 http://cdmd.cnki.com.cn/Article/CDMD-10593-1013359112.htm Zhang Yan. Gaussina Process Model for Forecasting Andidentifying Nonlinear Behaviorofunderground Engineering Rockmass Anddynamic Intelligent Feedback Analysis[D]. Nanning: Guangxi University, 2013 http://cdmd.cnki.com.cn/Article/CDMD-10593-1013359112.htm
[19]	苏国韶, 肖义龙.边坡可靠度分析的高斯过程方法[J].岩土工程学报, 2011, 33(6):916-920 http://www.wenkuxiazai.com/doc/b4caa98a71fe910ef12df8af.html Su Guoshao, Xiao Yilong. Gaussian Process Method for Slope Reliability Analysis[J]. Chinese Journal of Geotechnical Engineering, 2011, 33(6):916-920 http://www.wenkuxiazai.com/doc/b4caa98a71fe910ef12df8af.html
[20]	夏自能. 边坡位移非线性时间序列的髙斯过程预测方法[D]. 南宁: 广西大学, 2012 http://cdmd.cnki.com.cn/Article/CDMD-10593-1012495204.htm Xia Zineng. Forecasting Method of Nonlinear Time Series of Slopedeeormat Using Gaussian Process[D]. Nanning: Guangxi University, 2013 http://cdmd.cnki.com.cn/Article/CDMD-10593-1012495204.htm
[21]	Rasmussen C E, Williams C K. Gaussian Processes for Machine Learning[M]. Cambridge, MA:MIT Press, 2006

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Deformation Intelligent Prediction Model Based on Gaussian Process Regressionand Application

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