Estimations of Vertical Rail Bending Moments from Numerical Track Deflection Measurements Using Wavelet Analysis and Radial Basis Function Neural Networks

Abstract

A method for estimating rail bending moments from relative vertical track deflection data measured by a train-mounted measurement system is presented in this paper. The novelty of the current study is that complete estimations of rail positive and negative bending moments from track deflection measurements are conducted by using a wavelet multiresolution analysis in conjunction with the radial basis function neural network considering the effects of varying track modulus. The simulation results show that the proposed framework can effectively employ vertical track deflections to estimate both maximum positive and negative bending moments in rails, with the average estimation error being 6.22% (i.e., 2.82 kNm). Moreover, the study confirms the capability of the train-mounted vertical track deflection measurement system (commercially known as MRail) in evaluating the rail bending moments over long distances.