Bayesian System Identification of Rail–Sleeper–Ballast System in Time and Modal Domains: Comparative Study
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 3
Abstract
From the literature, time domain and modal domain data are commonly used in system identification and damage detection of various systems. This paper focuses on the comparison between time and modal domain system identification of a rail–sleeper–ballast system, which is modeled with the beam-on-springs theory. Linear elasticity is assumed in modal domain analyses, while the ballast layer is considered elastoplastic, in line with the behavior of ballast under large amplitude vibration in time domain analyses. A simple nonlinear model—the modified Ludwik model—was utilized to capture the strain-hardening behavior of ballast in the tensionless ballast springs. An enhanced Markov chain Monte Carlo (MCMC)-based Bayesian algorithm is utilized to handle the uncertainties associated with the identified system parameters from a probabilistic sense. This algorithm caters for cases that are unidentifiable and where the posterior probability density functions (PDF) are possibly nonGaussian. System identification was carried out using measured data obtained from impact hammer tests under laboratory conditions. Analysis results prove the applicability of the Bayesian algorithm in accurately identifying the severity and location of ballast damage in ballasted tracks. The results also showcase the limitations and merits of system identification of a highly damped system in the time and modal domains. It is concluded that the time domain is more favored than the modal domain for system identification of the considered rail–sleeper–ballast system owing to the effects of ballast nonlinearity under large amplitude vibration.
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Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The data, models, and codes include:
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Measured time domain acceleration data from impact hammer tests;
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The rail–sleeper–ballast models in MATLAB; and
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The MATLAB codes for calculating the system stiffness and mass matrices of the target structural system.
Acknowledgments
The work described in this paper was supported by two grants from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project Nos. CityU 11210517 (GRF 9042509) and R5020-18 (RIF 8799008)].
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Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 3 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jul 7, 2021
Accepted: Feb 22, 2022
Published online: Apr 22, 2022
Published in print: Sep 1, 2022
Discussion open until: Sep 22, 2022
ASCE Technical Topics:
- Algorithms
- Analysis (by type)
- Bayesian analysis
- Comparative studies
- Elastic analysis
- Engineering fundamentals
- Infrastructure
- Linear analysis
- Mathematics
- Methodology (by type)
- Modal analysis
- Rail transportation
- Railroad ballast
- Railroad tracks
- Research methods (by type)
- Statistical analysis (by type)
- Structural analysis
- Structural engineering
- Transportation engineering
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