Bridge pier settlement prediction in high-speed railway via autoregressive model based on robust weighted total least-squares

Abstract

The autoregressive model for time series prediction is a common method in settlement prediction. In the traditional parameter estimation of autoregressive model, least-squares (LS) is the method, which only considers the errors in the observation vector. However, the errors in the coefficient matrix have not been considered. To solve this issue, weighted total least-squares (WTLS) method is developed for parameter estimation. However, it does not consider the possible gross errors in observations, which may lead to a reduction in the robustness and reliability of parameter estimation. In order to solve this problem, in this study, robust WTLS (RWTLS) method is proposed to estimate parameters of autoregressive model for bridge pier settlement prediction in high-speed railway. A comparison with LS, robust LS (RLS) and WTLS methods is conducted for bridge pier settlement prediction and two sets of observed data are used in this evaluation. The results of experiments show that the variance components and the mean absolute values of predictive residuals obtained by WTLS and RWTLS methods are smaller than those by using LS and RLS methods in the case of modelling data without gross errors, and the variance component and the mean absolute value of predictive residuals obtained by RWTLS method is the smallest in the case of modelling data with gross errors. It shows that autoregressive model settlement prediction for bridge pier by using RWTLS method is more reliable and accurate than LS, RLS and WTLS methods in high-speed railway.