基于反射波特征小波分析的工程基础无损检测
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摘要
针对现有工程基础无损检测中反射波分析的不足,对Morlet和Marr小波及其小波变换进行了深入分析,指出它们在反射波分析上具有优势互补性。结合两个小波的优势,提出了Morlet小波域能量密度谱辨识动力学特征和Marr小波域模谱提取奇异特征的反射波分析方法,并构建了基于小波域香农熵的Morlet最优基小波确定技术。由反射波的动力学特征可以识别损伤模式,由反射波的奇异特征可以实施损伤定位,两者相结合构成了一条有机的基于反射波特征小波分析的工程基础精细无损检测技术路线。模型实验分析中概括归纳了工程基础的基本损伤模式,并验证了技术路线的可行、有效性。
Aiming at improving the analysis precision of reflection waves in nondestructive detection for civil engineering foundations,the characters of Morlet and Marr wavelets and the properties of their transforms are thoroughly investigated and the complementary advantages of these two wavelets in reflection wave analysis are discussed in detail. By combining the advantages of these two wavelets,a novel method for reflection wave analysis is proposed,which utilizes power density spectrum of Morlet wavelet to extract the dynamic characteristics of reflection waves and employs modulus spectrum of Marr wavelet to capture the singular features of reflection waves,in addition involves the key technique of using Shannon entropy of wavelet transform to determine the optimal mother wavelet of Morlet wavelet. Damage patterns may be identified in terms of the dynamic characteristics of reflection waves and damage positions may be located in terms of the singular features of reflection waves. Thus a promising technology is constituted for nondestructive detection of civil engineering foundations. The basic damage patterns of civil engineering foundations are demonstrated in the physical model experiment,and the feasibility and effectiveness of the technology are validated by both numerical and physical experiments.
Aiming at improving the analysis precision of reflection waves in nondestructive detection for civil engineering foundations,the characters of Morlet and Marr wavelets and the properties of their transforms are thoroughly investigated and the complementary advantages of these two wavelets in reflection wave analysis are discussed in detail. By combining the advantages of these two wavelets,a novel method for reflection wave analysis is proposed,which utilizes power density spectrum of Morlet wavelet to extract the dynamic characteristics of reflection waves and employs modulus spectrum of Marr wavelet to capture the singular features of reflection waves,in addition involves the key technique of using Shannon entropy of wavelet transform to determine the optimal mother wavelet of Morlet wavelet. Damage patterns may be identified in terms of the dynamic characteristics of reflection waves and damage positions may be located in terms of the singular features of reflection waves. Thus a promising technology is constituted for nondestructive detection of civil engineering foundations. The basic damage patterns of civil engineering foundations are demonstrated in the physical model experiment,and the feasibility and effectiveness of the technology are validated by both numerical and physical experiments.
引文
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[11]Hong J C,Kim Y Y.Determination of the optimal gabor wavelet shape for the best time-frequency localization using the entropy concept[J].Experimental Mechanics,2004,44(4):387-395.
[12]Staszewski W J.Identification of damping in MDOF systems using time-scale decompositions[J].Journal of Sound and Vibration,1997,203(2):283-292.
[2]Flandrin P.Time-Frequency/Time-Scale Analysis[M].New York:Academic Press,1999.
[3]Farge M.Wavelet transforms and their applications to turbulence[J].Annual Review of Fluid Mechanics,1992,24:395-457.
[4]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation[J].Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[5]Mallat S.A wavelet tour of signal processing[M].San Diego:Academic Press,1998.
[6]Kijewski T,Kareem A.Wavelet transforms for system identification in civil engineering[J].Computer-aided Civil Infrastructure Engineering,2003,18:339-355.
[7]曹茂森,任青文,王怀洪.基于小波与分形理论的地震异常检测[J].地球物理学报,2005,48(3):672-679.CAO Mao-sen,REN Qing-wen,WANG Huai-hong.A method of detecting seismic singularities using combined wavelet with fractal[J].Chinese Journal of geophysics,2005,48(3):672-679.
[8]曹茂森,任青文,毛伟兵.基于解耦子波和优化神经网络的大坝变形预测[J].岩石力学与工程学报,2005,24(10):1751-1757.CAO Mao-sen,REN Qing-wen,MAO Wei-bing.New prediction model for dam deformation based on decoupled wavelet and optimal neural networks[J].Chinese Journal of Rock Mechanics and Engineering,2005,24(10):1751-1757.
[9]许明,张永兴.锚固系统质量检测的小波分析法[J].岩土力学,2003,24(2):262-265.XU Ming,ZHANG Yong-xing.Wavelet analysis of quality inspection of anchorage system[J].Rock and Soil Mechanics,2003,24(2):262-265.
[10]张良均,王靖涛,李国成.小波变换在桩基完整性检波中的应用[J].岩石力学与工程学报,2002,21(11):1735-1738.ZHANG Liang-jun,WANG Jing-tao,LI Guo-cheng.Application of wavelet transform in integrity testing of piles[J].Chinese Journal of Rock Mechanics and Engineering,2002,21(11):1735-1738.
[11]Hong J C,Kim Y Y.Determination of the optimal gabor wavelet shape for the best time-frequency localization using the entropy concept[J].Experimental Mechanics,2004,44(4):387-395.
[12]Staszewski W J.Identification of damping in MDOF systems using time-scale decompositions[J].Journal of Sound and Vibration,1997,203(2):283-292.
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