A New Method for Separating Temperature Effect of Bridge Strain Monitoring

Abstract

Temperature has a significant influence on bridge strain monitoring data. To improve the accuracy of temperature effect separation in strain monitoring data, this paper proposes a temperature effect separation method comprising variational nonlinear chirp mode decomposition (VNCMD), principal component analysis (PCA) and blind source separation. Firstly, VNCMD was used to decompose the monitoring data of strain and temperature, and the intrinsic mode functions (IMF) of strain and temperature signals were obtained. Secondly, PCA was used to reduce the dimension of IMF, and the false components were eliminated to select the optimal components. After reducing the dimension, the components were used as the input of fast independent component analysis model for blind source separation. Finally, the feasibility and accuracy of the method was verified via the signal-to-noise ratio (SNR) in the simulated signal, and the separation results were evaluated using the Pearson correlation coefficient between the strain component and the corresponding temperature component in real bridge monitoring data. The proposed method performed better than the empirical mode decomposition (EMD) method. The signal-to-noise ratio (SNR) of VNCMD improved 51.80% for daily temperature difference effect and 32.41% for annual temperature difference effect in the numerical study, respectively; the correlation coefficients of VNCMD improved 52.90% for daily temperature difference effect and 4.26% for annual temperature difference effect in practical verification, respectively.