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Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model

Xin, Jingzhou, Zhou, Jianting, Yang, Simon, Li, Xiaoqing and Wang, Yu 2018. Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model. Sensors 18 (1) , p. 298. 10.3390/s18010298

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Abstract

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: MDPI Publishing
ISSN: 1424-8220
Date of First Compliant Deposit: 8 March 2018
Date of Acceptance: 15 January 2018
Last Modified: 18 Oct 2019 12:12
URI: http://orca.cf.ac.uk/id/eprint/109636

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