Abstract
Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR—especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles—an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.
Highlights
With the rapid deployment of transportation networks, an increased number of bridges have been built in the world
The inspection of curves of the separated signal components shown in this figure clearly highlight that: (1) as shown in Figure 8a, the separated signal, S1, with an inverted displacement is consistent with the original time-series displacement, X1, of the mid-span point of the Fengbei Bridge, which can be regarded as actual monitored data, and; (2) as shown in Figure 8b,c, there are some sudden changes in these two curves of the separate S2 and
There are some smaller fluctuations in these two curves, which may have been caused by the surrounding environment, such as wind thrusts and ground motions
Summary
With the rapid deployment of transportation networks, an increased number of bridges have been built in the world. The in method been applied fields resolution of up to 0.5 using GBSAR In this displacements study, we usedofa the newadjacent type of monitoring points have the same noisetechnology, information, especially for the time-series noise of the instantaneous ground-based microwave interferometry which uses multi-set displacements of vibrations the equipment caused bydomain passingwith vehicles with resolution a differentofspeed weight. In this study, information, especially for the noise of the instantaneous vibrations of the equipment itself caused by the obtained time-series displacement three adjacent monitoring points can be regarded as a signal linear passing vehicles with a different speedof and weight. Methodology vibrations of the equipment itself caused by passing vehicles with different speed and weight
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