Conventional planning of maintenance and renewal work for railway track is based on
heuristics and simple scheduling. The railway industry is now collecting a large amount of …

The railway sector has witnessed a significant surge in condition-based maintenance,
thanks to the proliferation of sensing technologies and data-driven methodologies, such as …

The fundamental problem of Bayesian model updating is identifying the posterior probability
density function (PDF) of uncertain model parameters. Markov chain Monte Carlo (MCMC) …

Bayesian damage identification method, due to its ability to consider the uncertainties, has
attracted much attention from researchers. However, there are two key issues to ensure the …

In this paper, an improved nonlinear model of railway ballast is proposed based on the
modified Ludwik model. The accuracy of model-based structural damage detection relies on …

Studying the dynamic characteristics and evolution laws of the ballast bed under low-
temperature, rain and snow environments has practical significance for the driving stability of …

This paper investigates the feasibility and practicability study on the use of Markov chain
Monte Carlo (MCMC)-based Bayesian approach for identifying the cement-emulsified …

The problem of optimal sensor placement for system identification and damage detection is
addressed by the development of a robust method based on Bayesian theory. Information …

This article reports the development of a methodology for detecting ballast damage under a
sleeper based on measured sleeper vibration following the Bayesian statistical system …

The present work is associated with Bayesian finite element (FE) model updating using
modal measurements based on maximizing the posterior probability instead of any sampling …