Innovative Track Monitoring Using FBG Sensors: Performance Under Static and Repetitive Loading

Abstract: In railroad engineering, track modulus is a key indicator of support conditions and is closely related to rail deflection magnitude. Lower modulus values correspond to larger deflection. Conventional monitoring systems are limited to measuring rail displacement at specific points, preventing them from effectively covering extended track lengths. An ongoing research study at Oklahoma State University aims to explore the application of Fiber Bragg Grating (FBG) sensors in a quasi-distributed arrangement to assess track conditions through continuous measurement of rail deflection. The primary objective is to measure axial strain along the rail and calculate the resultant rail response under dynamic train loading. Performance of the FBG-based monitoring system is influenced by train speed, which introduces load at specific frequencies.

The research method was to design a three-point bending test setup to simulate actual railroad track behavior in the laboratory. FBG sensors were utilized for axial strain measurement, while Linear Variable Differential Transformers (LVDTs) captured rail displacement. Engineering beam theory was applied to establish a relationship between axial strain and rail response. The performance of FBG-based calculations was rigorously evaluated under static and repetitive loading conditions in the lab. The purpose of static loading tests was to evaluate the accuracy and validity of theoretical calculations and provide a reference for assessing results from repetitive loading scenarios. Comparisons between FBG-based calculations and LVDT measurements confirmed the accuracy and reliability of the approach under varying loading conditions. Results demonstrated that integration of the FBG-based axial strain measurement and beam theory can effectively monitor the rail deflection. This research contributes to enhancing railroad track monitoring by demonstrating the potential of FBG-based sensors for multiplexed measurements over a long distance of the tracks. The findings suggest that this approach can be practical for assessing track support conditions and predicting rail response to train loads.