Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/116135
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Type: Journal article
Title: Railway ballast damage detection by Markov chain Monte Carlo-based Bayesian method
Author: Lam, H.
Yang, J.
Hu, Q.
Ng, C.
Citation: Structural Health Monitoring, 2018; 17(3):706-724
Publisher: Sage
Issue Date: 2018
ISSN: 1475-9217
1741-3168
Statement of
Responsibility: 
Heung F Lam, Jia H Yang, QinHu and Ching T Ng
Abstract: 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.
Keywords: Damage detection; Bayesian approach; Markov chain Monte Carlo; polynomial modelling; vibration; railway ballast
Rights: © The Author(s) 2017.
RMID: 0030083219
DOI: 10.1177/1475921717717106
Appears in Collections:Mechanical Engineering publications

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