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Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

机译：Kalman-ARIMA-GARCH模型的基于GNSS数据的桥梁结构变形预测

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摘要

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.
机译：桥梁是地面运输系统的重要组成部分。健康监测对于桥梁的安全性和使用寿命至关重要。使用传感技术从各种传感器获得了大量的结构信息，并且数据处理已成为一个具有挑战性的问题。为了提高基于数据挖掘的桥梁结构变形预测精度并准确评估桥梁结构性能演变的时变特征，提出了一种新的桥梁结构变形预测方法，该方法结合了卡尔曼滤波，自回归积分移动平均模型（ARIMA）和广义自回归条件异方差（GARCH）。首先，直接使用卡尔曼滤波器对原始变形数据进行预处理，以减少噪声。之后，建立线性递归ARIMA模型，以分析和预测结构变形。最后，引入非线性递归GARCH模型，以进一步提高预测的准确性。基于来自全球导航卫星系统（GNSS）变形监测系统的实测传感器数据的仿真结果表明：（1）卡尔曼滤波器能够对桥梁变形监测数据进行去噪； （2）提出的Kalman-ARIMA-GARCH模型的预测精度令人满意，其平均绝对误差随着预测步长的增加仅从3.402 mm增加到5.847 mm。 （3）与Kalman-ARIMA模型相比，Kalman-ARIMA-GARCH模型具有部分非线性特征（异方差），因此预测精度更高。使用该模型的五步预测的平均绝对误差提高了10.12％。本文为基于数据处理的结构行为预测提供了一种新方法，可以为基于传感技术的传感技术桥梁健康监测系统的预警奠定基础。

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