Displacement model error-based method for symmetrical cable-stayed bridge performance warning after eliminating variable load effects

This paper proposes a displacement model error-based performance warning method to detect structural anomalies in bridges. A displacement data pre-processing method based on multi-rate fusion method is proposed to modify displacement data with the same position acceleration data. A correlation model of the lateral wind speed and displacement was established to eliminate wind load effects. Then principal component analysis was used to eliminate traffic load effects. A new combined Mahalanobis-Euclidean warning index and Shewhart-CUSUM control chart are proposed. Moreover, a threshold setting method for the control chart based on the kernel density estimation technique is proposed. Three different thresholds are proposed to consider the effects of temperature, humidity, snow, rime, and icing. After the bridge warning is realized, the location index is derived from contribution analysis to indicate the location of bridge performance degradation. The results show that the proposed new warning index improves the warning rate compared with that of the two traditional warning indexes. When continuous small and medium shifts occur, the traditional control chart has a false-negative alarm, while the new combined Shewhart-CUSUM control chart can accurately realize the warning. The results of monitoring data analysis of a symmetric long-span cable-stayed bridge show that correlation modeling and principal component analysis can effectively eliminate wind and traffic effects, and the proposed new performance warning method can improve the warning rate and simultaneously monitor large shifts and small shifts, to accurately detect and locate the potential performance degradation points on a bridge.