  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.
