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

TL;DR

This paper demonstrates how the statistical finite element method (statFEM) can create a digital twin of a self-sensing structure, integrating sensor data and physics-based models to improve structural health monitoring and prediction accuracy.

Contribution

It introduces the application of statFEM for developing a digital twin that synthesizes sensor data with finite element models, accounting for uncertainties in a real-world infrastructure case study.

Findings

Predicts true system response considering sensor and model uncertainties.

Generates strain distribution predictions at unmeasured locations.

Enhances structural health monitoring and scenario analysis.

Abstract

The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element model, as 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 consistent and principled means for synthesising 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 27.34 m length located along the West Coast Mainline near Staffordshire in the UK is considered. Using strain data captured from fibre Bragg grating (FBG) 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 finite element model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modelled during the passage of a passenger train. The digital twin, because of its physics-based component, is able to generate reasonable strain distribution predictions at locations where no measurement data is 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 optimisation of sensor placement, and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data is available.