Sun, F., Febrianto, E. , Fernando, H., Butler, L. J., Cirak, F. and Hoult, N. A. (2023) Data-informed statistical finite element analysis of rail buckling. Computers and Structures, 289, 107163. (doi: 10.1016/j.compstruc.2023.107163)
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Abstract
In this paper, the statistical finite element method is developed further to synthesize distributed rail response data with nonlinear finite element model predictions within and outside the measured loading range. In the data-generating model of the statistical finite element method, the distributed sensing data is decomposed into a finite element model component, a model-reality mismatch component, and a noise component. Each component is considered uncertain and is represented as a Gaussian random vector with a corresponding prior density. The finite element prior density is updated using the Bayesian statistical framework in light of the distributed fiber optic sensing data. The calculated posterior density enables one to infer the true structural response. The required finite element prior density is determined by solving a conventional stochastic forward problem using a polynomial chaos expansion of random fields and a non-intrusive pseudo-spectral projection approach. In this paper, a lab test involving a section of a rail (i.e., a beam-column structural member) instrumented with distributed fiber optic sensors and subjected to axial load causing progressive lateral buckling is considered to demonstrate the use of the statistical finite element method to improve rail response prediction. The results show improved prediction of true rail strain in linear and nonlinear regimes compared with a pure finite element model-based prediction.
| Item Type: | Articles |
|---|---|
| Additional Information: | This project was supported in part by collaborative research funding from the National Research Council of Canada’s Artificial Intelligence for Logistics Program. This research was also supported by the Natural Sciences and Engineering Research Council of Canada and Transport Canada. E.F. and F. C. were supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the “Digital twins for complex engineering systems” theme within that grant, and The Alan Turing Institute. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Febrianto, Dr Eky |
| Authors: | Sun, F., Febrianto, E., Fernando, H., Butler, L. J., Cirak, F., and Hoult, N. A. |
| College/School: | College of Science and Engineering > School of Engineering > Infrastructure and Environment |
| Journal Name: | Computers and Structures |
| Publisher: | Elsevier |
| ISSN: | 0045-7949 |
| ISSN (Online): | 1879-2243 |
| Published Online: | 20 September 2023 |
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