A physics-informed machine learning model for reconstruction of dynamic
  loads
Gledson Rodrigo Tondo Igor Kavrakov Guido Morgenthal
Long-span bridges are subjected to a multitude of dynamic excitations during
their lifespan. To account for their effects on the structural system, several
load models are used during design to simulate the conditions the structure is
likely to experience. These models are based on different simplifying
assumptions and are generally guided by parameters that are stochastically
identified from measurement data, making their outputs inherently uncertain.
This paper presents a probabilistic physics-informed machine-learning framework
based on Gaussian process regression for reconstructing dynamic forces based on
measured deflections, velocities, or accelerations. The model can work with
incomplete and contaminated data and offers a natural regularization approach
to account for noise in the measurement system. An application of the developed
framework is given by an aerodynamic analysis of the Great Belt East Bridge.
The aerodynamic response is calculated numerically based on the quasi-steady
model, and the underlying forces are reconstructed using sparse and noisy
measurements. Results indicate a good agreement between the applied and the
predicted dynamic load and can be extended to calculate global responses and
the resulting internal forces. Uses of the developed framework include
validation of design models and assumptions, as well as prognosis of responses
to assist in damage detection and structural health monitoring.