A bridge damage monitoring and assessment strategy based on Gaussian mixture modelling

Abstract

For the bridges equipped with health monitoring systems, it is essential to precisely assess the health status of the bridge from the data with noise and outliers. An improved Gaussian mixture model clustering algorithm is used to process the obtained bridge strain sensor monitoring data and generate the characteristic data of clustering in order to improve the accuracy of data analysis of bridge health monitoring system. The cluster and feature data are obtained by Expectation Maximization process, and the isolated clusters are filtered out by the threshold parameters of weight and the Euclidean distance between clusters center. Based on the feature data, a scoring strategy is established to assess the bridge health and sensor status. The proposed strategy is used to analyze the monitoring data of a bridge in western China, and the analysis results show the availability of this strategy. Compared with the directly collected data, the processed bridge health scoring curve variation is smooth, which can reduce the influence of noise and abnormal data. The sensor status scoring curve can track changes in collected data and respond to different transformation scenarios. This shows that the proposed strategy can evaluate the state of this bridge and the sensors. provided a reference for bridge maintenance decision during the operational period.