Ambient Vibration

Assume that the accelerometer has the ideal response shown in Figure 4-223, with a measurement range of 2 g (32.2 ft/s ). We want to measure 1 g, but the ambient vibration level is +3 g. In this case, the accelerometer s indications are shaved and the mean value obtained is not 1 g but 0.5 g. The maximum acceleration due to vibrations which are not filtered mechanically, plus the... 

Keywords ambient vibration correlation function Duffing oscillator hydraulic jump information entropy modal identification optimal sensor placement spectral density structural health monitoring Wishart distribution... 

Much attention has also been devoted to modal identification without measuring the input time history. In particular, a lot of effort has been dedicated to the case of free vibration (or impulse response) and to the case of ambient vibration. In the former case, often time-domain methods based on auto-regressive moving average (ARMA) models are employed, using least squares as the core ingredient in their formulations. However, it was found that the least-squares method yields biased estimates [76], A number of methods have been developed to eliminate this bias, including the instrumental matrix with delayed observations method [76], the correlation fit method [275], the double least-squares method [114,202] and the total least-squares method [92]. A detailed comparison of these methods can be found in Cooper [61],... 

Another important practical category is the ambient vibration survey (AVS). It has attracted much interest because it offers a means of obtaining dynamic data in an economical and efficient manner, without requiring the setup of special dynamic experiments (e.g., actuators) which are usually costly, time consuming, and often obtrusive. In AVS, the naturally occurring vibration... 

Keywords ambient vibration Bayesian inference best estimaton conditional probability correlation function modal analysis modal identification nonstationary response seismic response structural health monitoring... 

Chapter 3 presented the Bayesian spectral density approach for the parametric identification of the multi-degree-of-freedom dynamical model using the measured response time history. The methodology is applicable for linear models and can also be utilized for weakly nonlinear models by obtaining the mean spectrum with equivalent linearization or strongly nonlinear models by obtaining the mean spectrum with simulations. The stationarity assumption in modal/model identification for an ambient vibration survey is common but there are many cases where the response measurements are better modeled as nonstationary, e.g., the structural response due to a series of wind gusts or seismic responses. In the literature, there are very few approaches which consider explicitly nonstationary response data, for example, [226,229]. Meanwhile, extension of the Bayesian spectral density approach for nonstationary response measurement is difficult since construction of the likelihood function is nontrivial in the frequency domain. Estimation of the time-dependent spectrum requires a number of data sets, which are associated with the same statistical time-frequency properties but this is impossible to achieve in practice. 

If the right hand side of Equation (4.13) is replaced by Yl =i r(tWA)gr(t)> the Bayesian time-domain methodology is applicable for excitations having different modulating functions, e.g., ambient vibrations with a series of wind gusts. 

Beck, J. L., May, B. S. and Polidori, D. C. Determination of modal parameters from ambient vibration data for structural health monitoring. In Proceedings of 1st World Conference on Structural Control (Pasadena, CA, 1994),pp. TA3 3-TA3 12. 

Brownjohn, J.M., Dumanoglu, A.A., Severn, S.T., Taylor, C.A.(1988) Ambient Vibration Survey of Humber Suspension Bridge. Research Report, University of Bristol. 

Ambient Vibration Survey of the Bosporus Suspension Bridge. Research Report, University of Bristol. 

Detailed investigations of the main dynamic characteristics have been performed for the prototype church in both orthogonal directions by applying the ambient vibration technique. Defined in this way were the natural frequencies 4.8 and 6.0 Hz for the N-S and E-W directions respectively. 

After the construction stage of the 5.8 m tall unreinforced building several ambient vibration tests for system identification were conducted in the uncracked condition. The results from 12 channels were imported in the system identification software ARTeMIS [Extractor 5.3] for frequency and mode shape identification. The 3-D shell of the building was modelled with 25 triangle elements. Comparisons with a finite element model using ETABS software [v. 9.1.1] verified the results. Only a 12 % difference between the first two frequencies was found, which represents the usual variations in the material parameters and construction details. 

Keywords Laser Doppler Vibrometer, Ambient vibration, Forced vibration. Local frequency, Global frequency. Local mode. Global mode... 

Ambient and forced vibrations were used to evaluate the frequency characteristics from power spectral densities. The global frequencies calculated from the areas of ambient vibration are shown in table B. The calculated local fiequencies are presented in Table C. Figures (12) and (13) Shows displacement and acceleration history of node (4) on the bridge slab. 

Figure (14) shows an example of the reccorded data due to ambient vibration.Using spectral estimation technique only the first global frequency has been determined from this data in the present work. The obtained results allow comparison with the F. E model. 

Reliable modal information can be obtained by output-only dynamic measurements, i.e. accelerations are dne to ambient influences. In the case of bridges, traffic under or on top of the bridge, besides wind pressures can be the cause of the induced vibrations. So closing bridges to apply controlled force excitation is not necessary. This makes ambient vibration monitoring suitable for continuous condition assessment. 

Zhang, Q.W. 2007. Statistical damage identification for bridges using ambient vibration data. 

Among the variety of fiber optic sensors, Fabry-Perot interferometer (FPI) sensors have shown the best performance in the frequency range up to 100 MHz because of their high sensitivity, broad bandwidth and excellent tolerance to low-frequency ambient vibration. Several FPI designs are used whereas intrinsic FPI sensors with flat mirrors show the best performance and are quite compatible with the mechanical structure of composites. Using this type of sensor for measurement of AEs, the detection bandwidth is 15 MHz to a few GHz. The minimum detectable phase of the current system was mainly limited by electronic noise 4 10 rad/Hz [66]. 

Jaishi, B., Ren, W. X. (2005). Structural finite el-ementmodel updating using ambient vibration test results. Structure Engineering, 737(4), 617-628. doi 10.1061/(ASCE)0733-9445(2005)131 4(617)... 

Ambient Vibration Testing of Cultural Heritage Structures... 

Other possible applications of ambient vibration-based modal analysis in the field of historic structures include periodic or continuous monitoring in order to evaluate the effects of repair interventions or to perform dynamics-based damage assessment (Ramos et al. 2010). 

---