  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 in making sense of the copious
amount of collected sensor data. This study 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 an instrumented railway bridge. The considered structure is a
steel skewed half-through bridge of 27.34 m length located along the West Coast
Mainline near Staffordshire in the UK. 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 optimization
of sensor placement, and performing more reliable what-if analyses at locations
and under loading scenarios for which no measurement data is available.
