【摘要】 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。更多还原

【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.更多还原