On the selection of the most plausible non-linear axial stress–strain model for railway ballast under different impulse magnitudes

Abstract

The effective maintenance and health monitoring of ballasted railway tracks, which involves the determination of differential settlement, track support stress and stiffness, and the strain-hardening property of ballast, is essential. The vertical stress–strain behavior of the ballast layer is primarily responsible for the irrecoverable strains and settlements in tracks, leading to further track degradation. This article reports the development of a series of applicable yet simple uniaxial models and the selection of the most plausible one for capturing the behavior of vertical stresses and strains in ballasts utilizing a set of measured vibration data of the rail–sleeper–ballast system from a Bayesian perspective. From the literature, the dynamic behavior of ballast can be divided into linear and non-linear regions. Under small amplitude vibration, the stress–strain property is linear and elastic, while the behavior becomes non-linear and inelastic once the elasticity limit is exceeded. By integrating the linear phase to some well-known non-linear engineering material laws, a list of new ballast stress–strain model classes was developed. An enhanced Markov chain Monte Carlo–based Bayesian scheme was utilized to explicitly handle the uncertainties in the model updating process, while the Bayesian model class selection method was employed to select the most plausible ballast stress–strain model class under the prevailing system conditions. The proposed methodology was verified using three sets of measured acceleration data from impact hammer tests on an in situ sleeper with simulated ballast damage. The obtained results suggest that the linear-elastic model is sufficient for small amplitude vibrations, while the modified Voce model is the most plausible amongst the investigated model classes for high impact load. The results also demonstrate the importance of the non-linear ballast model in ballast damage identification and the potential applicability of the selected ballast model in field track monitoring.

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

“…The parameters which are likely to change by local conditions, load patterns, or damage should be considered as uncertain parameters (Lam et al, 2020;Adeagbo et al, 2021Adeagbo et al, , 2022. The loose fastener only can influence the fastener stiffness a lot, and the values of the model parameters of the substructure (including the prefabricated slab, CA mortar layer and concrete base as well as the foundation) would not be changed too much, which can be referred to Hu et al (2021), therefore, only the scaling factors of the fastener stiffness will be treated as the uncertain model parameters in this study.…”

A data-driven method for real-time compaction quality evaluation of a cement-stabilized base layer

“…Although there are some problems in existing compaction indexes, vibration acceleration response is still an efficient tool to monitor compaction status. The authors had been successful in identifying the packing/compaction level (health status) of ballast underneath a railway sleeper using acceleration responses (Adeagbo et al, 2021a; Lam et al, 2020). It is well known that the vibratory drum and underlying compacted layer form a spring-mass-dashpot system.…”