Bayesian Modeling of Axial Strain-Hardening Behaviour in Railway Ballast

Adeagbo, Mujib Olamide; Lam, Heung-Fai

doi:10.1007/978-981-15-8079-6_102

Cited by 2 publications

(1 citation statement)

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“…5,12,26 The most common methods used in empirical models are the hyperbolic relationship 18,38 and the exponential/power law. 11,12,22 In conformity with previous works, 39 ballast models based on power law, exponential, hyperbolic, and linear functions were developed in this study.…”

Section: Non-linear Fe Model Of Ballasted Trackmentioning

confidence: 85%

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

Adeagbo

Lam

2021

Structural Health Monitoring

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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.

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