Efficient bridge steel bearing health monitoring using laser displacement sensors and wireless accelerometers

Abstract

Steel bearings have been commonly used to counteract induced loading from thermal and traffic conditions in numerous bridges. However, their effectiveness has been compromised due to aging and maintenance limitations, potentially impacting the overall bridge system performance. Existing monitoring techniques for detecting malfunctioning steel bearings lack automation and precision, making them inadequate for long-term and real-time bridge dynamics assessment. This study proposes a response-based approach to identify bearing malfunction by analyzing the traffic-induced response in the bearing vicinity. To implement this approach, laser displacement sensors and wireless acceleration sensors were employed to monitor both malfunctioning and well-functioning steel bridge bearings. Significant differences in bearing performance were observed through response analysis and comparison. Laser sensor measurements revealed larger vertical deflections in the girder at malfunctioned bearing under traffic loading. Moreover, the investigation of the acceleration response in the bearing locality indicated that bearing malfunction could alter the vibrational characteristics of the vicinity, significantly affecting Cross Power Spectral Density (CPSD) and cross-correlation. To quantitatively evaluate the performance of steel bearings, a Condition Score (CS) was introduced. The CS exhibited a strong correlation with bearing damage, providing valuable insights for maintenance and decision-making processes in bridge asset management. This study offers a comprehensive and automated method for identifying steel bridge bearing malfunction by utilizing advanced monitoring techniques and introducing the CS for assessment. The results obtained from this approach can enhance bridge maintenance strategies and contribute to effective bridge asset management.