Improvement of Bayesian Dynamic Linear Model for Predicting Missing Data of Bridges

The missing data in bridge operation will lead to the decline of the reliability of data analysis results. In this paper, the Bayesian dynamic linear model is improved by changing the parameter matrix of hidden state variables, and the model is optimized under the condition that the predefined variables are unchanged. The frequency of a strain measuring point of the bridge is taken as the observed value, and the collected frequency value of one month is used as the training set (the collection time interval is 30 minutes) to predict the data of the next week. By comparing the predicted result with the observed value, it is found that the absolute error is less than 14.05Hz and the relative error is less than 1.82% when the training frequency value varies from 756 Hz to 773.4 Hz