Investigation of AI-Based Analysis for Girder Deflection and Traffic Identification Using Bridge Vibration Time-Series Data

Abstract

This study focuses on time-series vibration data of bridges and applies AI-based analysis methods for assessing structural conditions and traffic environments. Acceleration response data were utilized to esti- mate girder deflection and classify traffic types, highlighting the potential for reliable data analysis and practical implementation. A machine learning model was constructed to correct errors arising from double integration in displacement estimation, improving accuracy. In addition, a method for detecting vehicle entry and exit times using longitudinal acceleration and an extended neural network approach for traffic classification were proposed. The results suggest that these approaches can enhance traffic census accuracy and promote more effective utilization of existing infrastructure.