Dynamic Monitoring of Rail Behavior under Passenger Train Loading Using Distributed Fiber Optic Sensors

Abstract

Increasing demand for railway transportation combined with more severe climate events, such as extreme heat, leads to an increased risk of degradation of track support and failure due to rail buckling. In this paper, distributed fiber optic sensing (DFOS) was used, for the first time, to assess track support degradation and the likelihood of rail dynamic buckling of curved rail sections. A monitoring campaign was conducted to measure the dynamic distributed strain response of a 9-m-long section of curved track during the passage of a passenger train. The distributed strain data were used to assess the axial strain and vertical bending curvature response during the passage of the train, and the distributed vertical curvature profile was then used to evaluate the wheel forces and track modulus of the monitored site using the Bayesian inference approach. The estimated wheel forces showed good agreement with the expected values, and the estimated track modulus was comparable to that measured using conventional techniques at other similar rail sites. With the estimated wheel forces and track modulus as inputs, a finite-element model developed in a commercial software package (i.e., ABAQUS) was used to assess the dynamic buckling capacity of the rail by considering the reduced rail lateral resistance due to the passing train. The results indicate that for this site, the passage of locomotives reduces the thermal buckling capacity by several degrees Celsius depending on the initial geometric imperfections in the rail, whereas passenger cars have negligible impact on the capacity.