Abstract
Bridge dynamic deflection is an important indicator of structure safety detection. Ground-based microwave interferometry is widely used in bridge dynamic deflection monitoring because it has the advantages of noncontact measurement and high precision. However, due to the influences of various factors, there are many noises in the obtained dynamic deflection of bridges obtained by ground-based microwave interferometry. To reduce the impacts of noise for bridge dynamic deflection obtained with ground-based microwave interferometry, this paper proposes a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) for the signal denoising of bridge dynamic deflection obtained by ground-based microwave interferometry. First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. Finally, the residual noises in the reconstructed signal were further eliminated using the morphological filter method. The results of both the simulated and on-site experiments showed that the proposed MF-ESMD method had a powerful signal denoising ability.
Highlights
Bridges have become one of the important elements of modern urban transportation
Erefore, in this study, to effectively eliminate the influences of noise in the dynamic deflection of the monitored bridges obtained by ground-based microwave interferometry, a morphology filter-assisted extreme-point symmetric mode decomposition (MF-Extreme-point symmetric mode decomposition (ESMD)) denoising method for bridge dynamic deflection obtained by ground-based microwave interferometry was proposed. e ESMD method was used to decompose the original obtained bridge dynamic deflection into a series of intrinsic mode functions (IMFs), and the noisedominant IMFs were further removed according to Spearman’s rho algorithm
E SNR was 26.4298 dB for the denoised signal using the MF-ensemble empirical mode decomposition (EEMD) method, which increased by 32.15% compared to the simulation signal Y(t). e SNR was 30.6619 dB for the denoised signal using the proposed MF-ESMD denoising method, which increased by 53.3% compared to the simulation signal Y(t). e results showed that the proposed MF-ESMD denoising method had a more powerful denoising ability for a nonlinear and nonstationary signal than the morphological filter method and the morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) method
Summary
Bridges have become one of the important elements of modern urban transportation. due to a combination of various factors such as age, environment, human behavior, overload, and geological activities, corrosion and cracks may occur on the surfaces of bridges and increase the deterioration of the bridges, which may lead to reducing the performance the bridges or even sudden collapses [1]. The mode mixing effect and the rough trend function can bring out noise with different scales in the decomposed IMFs, which may cause a loss of precision for the EMD denoised method [20, 23]. To solve this problem, the ensemble empirical mode decomposition (EEMD). Erefore, in this study, to effectively eliminate the influences of noise in the dynamic deflection of the monitored bridges obtained by ground-based microwave interferometry, a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) denoising method for bridge dynamic deflection obtained by ground-based microwave interferometry was proposed.
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