边坡的变形稳定性问题是土木工程建设中亟待解决的问题之一。大量研究表明,用实测的边坡位移时间序列预测边坡未来变形更为准确。但外界因素可能使数据产生误差,需去噪处理,才能使监测数据更有使用价值。结合时移小波去噪和灰色理论,对锦屏一级水电站边坡位移监测数据进行研究,提出了时移小波系数相关性去噪及小波-MGM(1,n)预测模型。该模型通过对小波尺度系数和近似系数的分解与重构来模拟真实信号,进而预测边坡的深度位移曲线。经验证预测曲线与实测曲线很接近,为边坡的治理和防护提供了一定的参考依据。
Abstract
Slope's deformation stability has been a pressing issue in civil engineering. A large number of studies have shown that it is accurate to predict slope deformation using measured displacement-time series. But de-noising of data is needed because of errors caused by external factors. In the present paper we put forward a time shift wavelet coefficient correlation de-noising and wavelet-MGM(1, n) model. The model is based on time shift wavelet theory and gray theory. The slope displacement data of Jinping first stage hydropower station is taken as an example. Through decomposition and reconstruction of wavelet scale coefficients and approximation coefficients, the real signal is simulated, and the slope elevation-displacement curve is predicted. Validation proves that the prediction curve is very close to the measured curve.
关键词
边坡稳定性 /
小波分析 /
去噪处理 /
灰色理论 /
数据处理
Key words
slope stability /
wavelet analysis /
de-noising processing /
grey theory /
data processing
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 王 波.一种改进的灰色预测模型分析[J].太原城市职业技术学院院报,2012,(6):166-167.(WANG Bo. An Improved Grey Prediction Model Analysis[J].Journal of Taiyuan Urban Vocational College,2012,(6):166-167.(in Chinese))
[2] 邓聚龙.灰色系统基本方法[M].武汉:华中理工大学出版社,1987.(DENG Ju-long. Grey System Method [M].Wuhan:Huazhong University of Science and Technology Press,1987.(in Chinese))
[3] 毛亚纯,贾崴崴,沙成满,等.基于小波分析的灰色预测法预测边坡变形[J].矿业工程,2010,8(6):17-20(MAO Ya-chun,JIA Wei-wei,SHA Cheng-man,et al.Wavelet Analysis Based Grey Model Applied in Prediction of Slope Deformation[J].Journal of Mining Engineering,2010,8(6):17-20.(in Chinese))
[4] 吴 伟,蔡培升.基于Matlab的小波去噪仿真[J].信息与电子工程,2008,6(3):202-222,229.(WU wei,CAI Pei-sheng.Simulation of Wavelet Denoising Based on MatLab[J].Information and Electronic Engineering,2008,6(3):220-222,229.(in Chinese))
[5] MALLAT S,HWANG W L.Singularity Detection and Processing with Wavelets[J].IEEE Transactions on Information Theory,1992,38(2):617-643.
[6] XU Y S,WEAVER J B,HEALY D M,et al.Wavelet Transform Domain Filters:A Spatially Selective Noise Filtration Technique[J].IEEE Transactions on Image Processing,1994,3(6):747-758.
[7] 徐建江,裴 亮.基于小波分析的MGM(1,N)模型在大坝监测系统中的应用[J].中国农村水利水电,2013,3(7):108-110,117.(XU Jian-jiang,PEI Liang.The Application of Dan Monitoring Systems Based on MGM(1,N)Model on Wavelet Analysis[J].Journal of China Rural Water and Hydropower,2013,3(7):108-110,117.(in Chinese))
基金
国家自然科学基金项目(21372329);中央高校基本科研业务费专项资金资助(2015B06014)
PDF(3287 KB)
