Q. Hu

Use of Measured Vibration of In-Situ Sleeper for Detecting Underlying Railway Ballast Damage

The identification of railway ballast damage under a concrete sleeper is investigated by following the Bayesian approach. The use of a discrete modeling method to capture the distribution of ballast stiffness under the sleeper introduces artificial stiffness discontinuities between different ballast regions. This increases the effects of modeling errors and reduces the accuracy of the ballast damage detection results. In this paper, a continuous modeling method was developed to overcome this difficulty. The uncertainties induced by modeling error and measurement noise are the major difficulties of vibration-based damage detection methods. In the proposed methodology, Bayesian probabilistic approach is adopted to explicitly address the uncertainties associated with the identified model parameters. In the model updating process, the stiffness of the ballast foundation is assumed to be continuous along the sleeper by using a polynomial of order N. One of the contributions of this paper is to calculate the order N conditional on a given set of measurement utilizing the Bayesian model class selection method. The proposed ballast damage detection methodology was verified with vibration data obtained from a segment of full-scale ballasted track under laboratory conditions, and the experimental verification results are very encouraging showing that it is possible to use the Bayesian approach along with the newly developed continuous modeling method for the purpose of ballast damage detection.

Railway ballast damage detection by Markov chain Monte Carlo-based Bayesian method

This article reports the development of a Bayesian method for assessing the damage status of railway ballast under a concrete sleeper based on vibration data of the in situ sleeper. One of the important contributions of the proposed method is to describe the variation of stiffness distribution of ballast using Lagrange polynomial, for which the order of the polynomial is decided by the Bayesian approach. The probability of various orders of polynomial conditional on a given set of measured vibration data is calculated. The order of polynomial with the highest probability is selected as the most plausible order and used for updating the ballast stiffness distribution. Due to the uncertain nature of railway ballast, the corresponding model updating problem is usually unidentifiable. To ensure the applicability of the proposed method even in unidentifiable cases, a computational efficient Markov chain Monte Carlo–based Bayesian method was employed in the proposed method for generating a set of samples in the important region of parameter space to approximate the posterior (updated) probability density function of ballast stiffness. The proposed ballast damage detection method was verified with roving hammer test data from a segment of full-scale ballasted track. The experimental verification results positively show the potential of the proposed method in ballast damage detection.