Fastener partial looseness identification of the ballastless track by a time-domain Bayesian approach

Abstract

The fastener system is an important component of the ballastless track, and the fastener looseness threatens the safety of the railway track. This paper proposes a Bayesian model updating method integrated with a Python–Abaqus interface framework to identify the fastener partial looseness of the ballastless track via vibration data. By following the technical standards of Chinese Railway Industry, a laboratory scaled ballastless track model was constructed for the demonstration and verification of the proposed methodology. Not only the damage location can be accurately identified, but also the damage severity can be evaluated. In addition, the associated posterior uncertainties of the identified results can be quantified by calculating the posterior probability density functions of all uncertain model parameters, which can provide valuable information to engineers for ballastless track system damage detection.