Comparative evaluation of data assimilation and machine learning for bridge displacement estimation using RTK-GNSS: a perspective on robust prediction and uncertainty quantification

Abstract

The aging of bridge infrastructure requires efficient monitoring methods to support timely maintenance. Among these, Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) offers real-time displacement monitoring but is highly sensitive to noise, requiring robust estimation techniques. This study compares two representative approaches for displacement estimation: Ensemble Kalman Filter (EnKF), a data assimilation method, and Random Forest (RF), a machine learning algorithm.

Long-term RTK-GNSS observations were conducted on the Shin-Yahata River Bridge, a continuous curved steel box girder bridge. After outlier removal, three-dimensional displacements were estimated using both EnKF and RF. Their accuracy was validated by comparison with static GNSS data and displacement sensors using RMSE, MAE, Max Error, and correlation coefficient r.

The results show that both methods can reproduce displacement trends with high accuracy. EnKF provides stable performance with real-time adaptability, while RF captures nonlinear relationships between environmental changes and structural response. This comparison clarifies the strengths and limitations of each method and offers practical guidance for selecting estimation techniques based on monitoring objectives. The study also contributes to the foundation for integrating GNSS-based monitoring with AI and digital twin technologies in bridge maintenance.