Real-Time Bridge Deflection Prediction Based on a Novel Bayesian Dynamic Difference Model and Nonstationary Data

Abstract

Accurate deflection prediction of in-service bridges can be used to assess the overall structural stiffness and detect abnormal states in advance. The bridge structures, especially long-span bridges, experience varying environmental and operational conditions, including temperature, humidity, wind excitation, and traffic loads, as well as long-term material deterioration and stiffness degradation mechanisms, and therefore, their deformation behavior shows complex variation phenomena, which pose challenges to many current deflection prediction methods. To address this subject, a Bayesian dynamic difference model (BDDM) to predict bridge deflection behavior online is proposed in this paper, explicitly considering the effect of the nonstationarity of time series data under varying environmental and operational conditions. A novel dynamic difference model is first proposed to include the nonstationary residual term and provide a linear approximation of a complex nonlinear process. Then, the formulas for recursively updating the dynamic difference model based on Bayesian inference are proposed. The proposed method is first validated through a numerical application using simulated nonstationary time series data with a nonlinear trend, indicating that it can adaptively capture nonstationary variations, update noise variance estimations, and improve prediction accuracy. To further demonstrate its performance, the BDDM is employed to predict the daily maximum deflection of a real-world cable-stayed bridge using measured data, and its performance is compared with several existing methods. The findings reveal that the proposed method outperforms other methods in terms of prediction accuracy, and can be potentially implemented for an online monitoring and early warning system.