Abstract
Accurate and timely identification of modal parameters of long-span bridges is important for bridge health monitoring and wind tunnel tests. Wavelet analysis is one of the most advantageous methods for identification because of its good localization characteristics in both time and frequency domain. In recent years, the wavelet method has been applied more frequently in parameter identification of linear and nonlinear systems. In this article, based on wavelet ridges and wavelet skeleton, the improved modal parameter identification method was studied. To find the appropriate time-frequency resolution, an optimal wavelet basis design principle based on minimum Shannon entropy was proposed. Aiming at endpoint effect in wavelet transform, a prediction continuation method based on support vector machine (SVM) was proposed, which can effectively suppress the endpoint effect of the extended samples. In view of the fact that the ridges of metric matrices obtained by the traditional crazy climber algorithm cannot fully reflect the distribution of ridges of modulus value matrices of wavelet coefficients, an improved high-precision crazy climber algorithm was put forward to accurately identify the position of the ridge of wavelet coefficients. Finally, taking a long-span cable-stayed bridge and a long-span suspension bridge as the engineering background, improved continuous wavelet transform (CWT) was applied to modal parameter identification of bridge under ambient excitation. The modal parameters such as modal frequency, damping ratio, and mode shape were obtained. Compared with the calculation value of the numerical simulation of long-span cable-stayed bridge and wind tunnel test of long-span suspension bridge, the reliability of CWT for modal parameter identification of long-span bridges under ambient excitation was verified.
Highlights
E structural modal parameters, which could affect the design of structures, include natural frequencies, damping ratios, and mode shape vectors [2, 3]. e development of related technology has a very positive significance for the bridge structure in the complex environment [4]. e accurate identification of modes is beneficial to the dynamic response analysis of bridges in various environments [5,6,7]. ere were various techniques used for signal decomposition in determining the bridge modal parameter identification, such as empirical mode decomposition (EMD), stochastic subspace identification (SSI), and wavelet transform (WT)
To overwhelm the scale separation problem, a new method based on the statistical studies of white noise called ensemble EMD (EEMD) was introduced by Wu and Huang [11]. e EEMD defines the intrinsic mode function (IMF) component as a mean of an ensemble of trails having signal as well as white noise of finite amplitude [11]. e new concept of white noise was investigated ([12,13,14])
For the first-order modal, the suppress effect of the endpoint effect is increased by 41.5%
Summary
In the last few decades, for the innovative tools for understanding and the optimization of design and assessment of structural health, the modal parameter identification is becoming more and more important [1]. Ere were various techniques used for signal decomposition in determining the bridge modal parameter identification, such as empirical mode decomposition (EMD), stochastic subspace identification (SSI), and wavelet transform (WT). To overwhelm the scale separation problem, a new method based on the statistical studies of white noise called ensemble EMD (EEMD) was introduced by Wu and Huang [11]. Based on a long-span cable-stayed bridge, which was excited by typhoon Haikui, the model parameters including frequency and damping ratio were identified using the WT method [28]. A long-span cable-stayed bridge with numerical simulation and a long-span suspension bridge for wind tunnel test were taken as the engineering background to verify the improved continuous wavelet transform method for modal parameter identification
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