Digital twinning of self-sensing structures using the statistical finite element method

Febrianto, E. , Butler, L., Girolami, M. and Cirak, F. (2022) Digital twinning of self-sensing structures using the statistical finite element method. Data-Centric Engineering, 3, e31. (doi: 10.1017/dce.2022.28)

Abstract

The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element (FE) model, as commonly used in design and construction, can help make sense of the copious amount of collected sensor data. This paper demonstrates the application of the statistical finite element method (statFEM), which provides a principled means of synthesizing data and physics-based models, in developing a digital twin of a self-sensing structure. As a case study, an instrumented steel railway bridge of 27.34m length located along the West Coast Mainline near Staffordshire in the UK is considered. Using strain data captured from fiber Bragg grating sensors at 108 locations along the bridge superstructure, statFEM can predict the “true” system response while taking into account the uncertainties in sensor readings, applied loading, and FE model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modeled during the passage of a passenger train. The statFEM digital twin is able to generate reasonable strain distribution predictions at locations where no measurement data are available, including at several points along the main I-beams and on structural elements on which sensors are not even installed. The implications for long-term structural health monitoring and assessment include optimization of sensor placement and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data are available.

• Item Type: Articles
• Additional Information: Funding Statement. This work was 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: Febrianto, E., Butler, L., Girolami, M., and Cirak, F.
• College/School: College of Science and Engineering > School of Engineering > Infrastructure and Environment
• Journal Name: Data-Centric Engineering
• Publisher: Cambridge University Press
• ISSN: 2632-6736
• ISSN (Online): 2632-6736
• Copyright Holders: Copyright: © The Author(s) 2022
• First Published: First published in Data-Centric Engineering 3: e31
• Publisher Policy: Reproduced under a Creative Commons licence