Abstract
Data‐driven stochastic subspace identification (DATA‐SSI) is frequently applied to bridge modal parameter identification because of its high stability and accuracy. However, the existence of abnormal data and noise components may make the identification result of DATA‐SSI unreliable. In order to achieve a reliable identification result of the bridge modal parameter, a data inspecting and denoising method based on exploratory data analysis (EDA) and morphological filter (MF) was proposed for DATA‐SSI. First, EDA was adopted to inspect the data quality for removing the data measured from malfunctioning sensors. Then, MF along with an automated structural element (SE) size determination technique was adopted to suppress the noise components. At last, DATA‐SSI and stabilization diagram were applied to identify and exhibit the bridge modal parameter. A model bridge and a real bridge were used to verify the effectiveness of the proposed method. The comparison of the identification results of the original data and improved data was made. The results show that the identification results obtained with the improved data are more accurate, stable, and reliable.
Highlights
Bridge is a critical structure of the whole transportation network, and it is vital for engineers to be aware of its operational state [1]
DATA-SSI was adopted by Brincker on the Great Belt Bridge for modal parameter identification, and the results showed that DATA-SSI was appropriate for identifying closely spaced modes [10]
A composite morphological filter (MF) combined with genetic programming training algorithm was developed by Yang and Li, the method was adopted on simulated and real MRI data to eliminate the noise components, and the results showed that the method was sensitive to noise especially when the noise level was high [26]
Summary
Bridge is a critical structure of the whole transportation network, and it is vital for engineers to be aware of its operational state [1]. DATASSI was applied by Altunisik on a scaled girder bridge for extracting modal parameters, and the results showed that the method had a good ability for identifying frequencies and mode shapes [7]. DATA-SSI was adopted by Brincker on the Great Belt Bridge for modal parameter identification, and the results showed that DATA-SSI was appropriate for identifying closely spaced modes [10]. In order to get a reliable bridge modal parameter identification result, efficient data inspecting and denoising techniques are needed. According to the aforementioned research studies, MF is an efficient tool for filtering the noise components in data and it is a promising solution for bridge monitoring data denoising. A data inspecting and denoising method based on EDA and MF for DATA-SSI was proposed.
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