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

Jingzhou Xin1, Jianting Zhou2, Simon X Yang3

  • 1School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China. xinjz@mails.cqjtu.edu.cn.

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Summary

This study introduces a novel Kalman-ARIMA-GARCH model for accurate bridge deformation prediction. The integrated approach enhances structural health monitoring and early warning systems by improving data processing and prediction accuracy.

Keywords:
bridge engineeringbridge sensor datadeformation predictionstructural health monitoring

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Area of Science:

  • Civil Engineering
  • Structural Health Monitoring
  • Data Science

Background:

  • Bridge health monitoring is crucial for transportation safety and longevity.
  • Processing large volumes of sensor data for structural analysis presents significant challenges.
  • Accurate prediction of bridge deformation is essential for evaluating performance evolution.

Purpose of the Study:

  • To develop an advanced data-driven method for predicting bridge structure deformation.
  • To enhance the accuracy of structural performance evaluation using data mining techniques.
  • To improve the reliability of bridge health monitoring systems.

Main Methods:

  • Utilized Kalman filter for preprocessing and noise reduction of raw deformation data.
  • Applied autoregressive integrated moving average (ARIMA) model for linear prediction of structural deformation.
  • Integrated generalized autoregressive conditional heteroskedasticity (GARCH) model to capture nonlinear characteristics and improve prediction accuracy.

Main Results:

  • Kalman filter effectively denoises Global Navigation Satellite System (GNSS) deformation monitoring data.
  • The proposed Kalman-ARIMA-GARCH model demonstrates satisfactory prediction accuracy, with mean absolute error increasing from 3.402 mm to 5.847 mm.
  • The Kalman-ARIMA-GARCH model improved prediction accuracy by 10.12% compared to the Kalman-ARIMA model by incorporating heteroscedasticity.

Conclusions:

  • The Kalman filter is effective for denoising bridge deformation data.
  • The integrated Kalman-ARIMA-GARCH model offers superior prediction accuracy for bridge deformation.
  • This method provides a foundation for advanced early warning systems in bridge health monitoring.