An anomaly pattern detection for bridge structural response considering time-varying temperature coefficients

Abstract

To improve the detection rate in the bridge anomaly detection, this study proposes an anomaly pattern detection method based on the time-varying temperature–displacement relationship. First, the empirical wavelet transform method is used to extract thermal-induced displacement components. Then, based on the daily variation of solar radiation, a pattern is defined as three groups of regression parameters associated with the temperature–displacement relationships. To measure the uncertainty in the anomaly detection, distributions of the parameters in the defined pattern are determined by Bayesian estimation. To overcome occasionality, a new index — weekly mean detection rate is defined. The effectiveness of the proposed temperature-driven anomaly pattern detection method is validated by using actual girder end displacements from a large span suspension bridge in China. As a result, the anomalous boundary condition is detected with a weekly mean detection rate of 0.91. Whilst, the weekly mean detection rate is 0.67 when applying the conventional anomaly detection method without considering the time-varying factor. Finally, the detection rate and the false detection rate of the proposed method are compared with those of cointegration and moving principle component analysis methods. The proposed method has a higher detection rate and a lower false detection rate for the anomalous boundary condition.