Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model. (1st August 2019)

• Record Type: Journal Article
• Authors: Wang, Hao; Zhang, Yi-Ming; Mao, Jian-Xiao; Wan, Hua-Ping; Tao, Tian-You; Zhu, Qing-Xin
• Is Part Of: Engineering structures. Volume 192(2019)
• Page Start: 220
• Page End: 232
• Publication Date: 2019-08-01
• Subjects: Bayesian dynamic linear model -- Temperature-induced strain -- Strain forecast -- Long-span bridge -- Structural health monitoring; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2019.05.006
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: An improved BDLM-based method is presented for modeling and forecasting of TIS. Trend, seasonal, regressive and AR components are considered in BDLM. The present BDLM-based method allows for probabilistic forecasts. BDLM shows better forecasting performance than AR, MLR and BDLM without AR component. Abstract: Temperature-driven baseline is highly responsive to anomalous structural behavior of long-span bridges, which means that the discrepancy between the measured and forecasting temperature-induced strain (TIS) can be examined for anomalies. In this regard, it is important to guarantee the accuracy of the forecasting TIS responses for reliable assessment of structural performance. Bayesian dynamic linear model (BDLM) has shown a promising application in the field of structural health monitoring. Traditionally, BDLM is used to forecast structural responses by utilizing its trend form, seasonal form, regression form, or combination of the three forms. However, different features of time series cannot be totally captured by these forms, which would undermine the accuracy of BDLM. To improve the computational accuracy, an improved BDLM, which considers an autoregressive (AR) component in addition to the trend, seasonal and regression components, is presented in this paper. Specifically, the AR component is able to model the component which cannot be captured by other three components. The real-time monitoring data collected from a long-span cable-stayed bridge is utilized to demonstrate the feasibility of the improved BDLM-based method. In particular, the present BDLM-based method allows for probabilistic forecasts, offering substantial information about the target TIS response, such as mean and confidence interval. Results show that the improved BDLM is capable of capturing the relationship between temperature and TIS. Compared to the AR model, multiple linear regression (MLR) model and BDLM without the AR component, the improved BDLM shows better forecasting performance in modeling and forecasting the TIS of a long-span bridge.