Fundamental study on the abnormality detection for maintenance of continuous curved steel box girder bridge based on GNSS displacement monitoring

Abstract

In recent years, the use of satellite technologies such as GNSS and SAR has attracted increasing attention as a method for displacement measurement in infrastructure structural health monitoring (SHM) utilizing ICT and IoT. Although the removal of noise from observational data remains a critical challenge, these technologies enable continuous, long-term, and three-dimensional deformation monitoring, offering significant promise for bridge maintenance and early anomaly detection.

This study focuses on a five-span continuous curved steel box girder bridge and investigates three-dimensional displacements measured using RTK-GNSS. A noise reduction technique based on Kalman filtering was developed to improve measurement accuracy, and the resulting data were used to analyze structural deformation behavior. Notably, the GNSS data revealed abnormal deformation trends during the summer, prompting the development of a detection mechanism to identify such anomalies in real time. This paper presents the methodology, implementation, and evaluation of the proposed system, demonstrating the potential of GNSS-based monitoring for advanced bridge lifecycle management.