Stress Evaluation in Rails Based on Vibration Data and Artificial Intelligence

Abstract

An inspection technique for the estimation of longitudinal stress and rail neutral temperature (RNT) in continuous welded rails (CWR) is presented. The technique is based on the use of finite element analysis (FEA), vibration measurements, and machine learning (ML). The FEA is used to establish the dependency of the vibration characteristics (mode shapes and frequencies) of any given CWR to the conditions of the track and the longitudinal stress of the rail. The dependency is then used to train an ML algorithm that is tested using experimental data obtained with two accelerometers bonded to the rail of interest, during a field test conducted in Colorado, U.S. A commercial finite element (FE) software was used to model the rail track as a short segment repeated indefinitely and under varying boundary conditions and stress. Three ML models were developed using hyperparameter search optimization techniques and k-fold cross-validation to infer the stress or RNT from the frequencies of vibration extracted from the time waveforms recorded by the accelerometers. The results demonstrated that the success of the technique is highly dependent on the accuracy of the FE model. The results also demonstrated that ML was mostly able to learn from the experimental data and successfully predict the neutral temperature.