The continuous welded rail (CWR) has been widely adopted in modern railways for its ability to support high transport speeds and reduce maintenance compared with jointed tracks. However, CWR is susceptible to internal stress due to restrained of its thermal expansion and contraction along the rail’s axial direction. During hot conditions, rail expansion is constrained, generating significant axial forces that can lead to thermal buckling depending on track conditions. The magnitude and direction of these built-up stresses are governed by the rail temperature relative to the rail neutral temperature (RNT). Accurately estimating RNT, and thus the associated thermal forces, without baseline measurements remains a long-standing challenge in railway engineering. In this study, we present recent advancements in rail thermal force estimation using intrinsic local resonances in rails. The results demonstrate that the frequencies of these local resonances, specifically, the zero-group-velocity modes and cutoff-frequency resonances whose energy is confined near the excitation zone, are insensitive to the presence of rail supports yet highly sensitive to variations in rail temperature and axial load. This unique characteristic makes them highly promising for accurate and non-intrusive RNT estimation. An automatic impulse-dynamic testing system was developed and mounted on a mobile platform to extract rail local resonances from a fully instrumented site at the FAST loop in MxV Rail, where strain gauges and thermocouples provide ground-truth RNT and rail longitudinal force data. Two complementary experiments were conducted: a stationary test that continuously collected local resonance data at a fixed location with known RNT and rail forces, and an in-motion test using the mobile sensing platform with piezoelectric excitation and contactless microphones to identify resonance modes along the rail. Probabilistic machine learning algorithms were developed to predict rail thermal forces at the instrumented site using two directly measurable inputs: rail temperature and the frequencies of rail local resonances. The performance of the proposed framework was evaluated by comparing the predicted thermal forces with the ground-truth data, demonstrating its capability for reliable and data-driven RNT estimation.

This short article describes one of the latest advancements of a monitoring/inspection technique for the estimation of localized longitudinal stress in continuous welded rails (CWR). The technique is based on the use of vibration measurements and machine learning (ML). A finite element analysis is conducted to model the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics of the rail. The results of the numerical analysis are used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers polled on the track of interest. The proposed technique was tested in the field. A commercial FEM software was used to model the rail track as a short rail segment repeated indefinitely and under varying boundary conditions and stress. A ML model was developed to infer the rail neutral temperature and the local resistance of rails to vertical and lateral displacement. The results of the experiments demonstrated that the success of the technique is dependent on the accuracy of the model and the ability to properly label the modes of the detected frequencies. This study builds upon previous research conducted at the University of Pittsburgh and the interested reader is referred to previous publications from the authors for more details about the proposed technique.

Track buckling at elevated temperatures remains a leading cause of train derailment in the US, and efforts to develop effective, nondestructive methodologies for assessing in-situ stress in the rail have been explored for decades. This presentation will discuss recent advancements in using ultrasonic birefringence measurements for assessing in-situ stresses in rail and determining the rail neutral temperature (RNT), i.e., the temperature at which the rail is unstressed. The ultrasonic birefringence approach compares the velocity of orthogonally polarized shear waves propagating through the material to assess average stresses. The research to be presented is using Electromagnetic Acoustic Transducers (EMATs) to perform ultrasonic stress measurements through the rail web in order to estimate the RNT. The EMATs allow for non-contact measurements that have the potential for in-motion stress measurements. The linear relationship between the acoustic birefringence of the material and the axial stress is used, along with a unique approach for finding the average natural or stress-free birefringence, to make accurate estimates of the in-situ stress and the RNT. Results from laboratory development and field testing at the MxV Facility for Accelerated Service Testing (FAST) track will be presented.