The synthesis of data from instrumented structures and physics-based models via Gaussian processes
File(s)
Accepted version
OA Location
Author(s)
Gregory, Alastair
Lau, Din
Girolami, Mark
Butler, Liam
Elshafie, Mohammed
Type
Journal Article
Abstract
At the heart of structural engineering research is the use of data obtained from physical structures such as bridges, viaducts and buildings. These data can represent how the structure responds to various stimuli over time when in operation. Many models have been proposed in literature to represent such data, such as linear statistical models. Based upon these models, the health of the structure is reasoned about, e.g. through damage indices, changes in likelihood and statistical parameter estimates. On the other hand, physics-based models are typically used when designing structures to predict how the structure will respond to operational stimuli. These models represent how the structure responds to stimuli under idealised conditions. What remains unclear in the literature is how to combine the observed data with information from the idealised physics-based model into a model that describes the responses of the operational structure. This paper introduces a new approach which fuses together observed data from a physical structure during operation and information from a mathematical model. The observed data are combined with data simulated from the physics-based model using a multi-output Gaussian process formulation. The novelty of this method is how the information from observed data and the physics-based model is balanced to obtain a representative model of the structures response to stimuli. We present our method using data obtained from a fibre-optic sensor network installed on experimental railway sleepers. The curvature of the sleeper at sensor and also non-sensor locations is modelled, guided by the mathematical representation. We discuss how this approach can be used to reason about changes in the structures behaviour over time using simulations and experimental data. The results show that the methodology can accurately detect such changes. They also indicate that the methodology can infer information about changes in the parameters within the physics-based model, including those governing components of the structure not measured directly by sensors such as the ballast foundation.
Date Issued
2019-09-01
Date Acceptance
2019-04-29
Citation
Journal of Computational Physics, 2019, 392, pp.248-265
ISSN
0021-9991
Publisher
Elsevier
Start Page
248
End Page
265
Journal / Book Title
Journal of Computational Physics
Volume
392
Copyright Statement
© 2019 Elsevier Inc. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
The Alan Turing Institute
Royal Academy Of Engineering
Grant Number
ATIGA001
RCSRF1718/6/34
Subjects
01 Mathematical Sciences
02 Physical Sciences
09 Engineering
Applied Mathematics
Publication Status
Published
Date Publish Online
2019-05-06