Sequential Bayesian estimation of segmental paramters in cantilever erection practice of bridges

In consideration of construction correlation between segments in cantilever erection practice of bridges, Gaussian random process model is introduced in this paper to represent the distribution of segmental parameters. Subsequently, through system rebuilding and maximum a posteriori criteria, a Bayesian formula is derived for sequentially estimating the mean values and standard deviations of random segmental parameters. It is shown that both the common-used least square estimation method and the Kalman filter estimation method are special cases of the proposed method. Finally, application of the proposed method in a long-span PC cable-stayed bridge for estimating self-weights of segmental girders is presented, and effectiveness of the proposed method in updating and predicting parameters shown.