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Long-span bridges are subjected to a multitude of dynamic excitations duringtheir lifespan. To account for their effects on the structural system, severalload models are used during design to simulate the conditions the structure islikely to experience. These models are based on different simplifyingassumptions and are generally guided by parameters that are stochasticallyidentified from measurement data, making their outputs inherently uncertain.This paper presents a probabilistic physics-informed machine-learning frameworkbased on Gaussian process regression for reconstructing dynamic forces based onmeasured deflections, velocities, or accelerations. The model can work withincomplete and contaminated data and offers a natural regularization approachto account for noise in the measurement system. An application of the developedframework is given by an aerodynamic analysis of the Great Belt East Bridge.The aerodynamic response is calculated numerically based on the quasi-steadymodel, and the underlying forces are reconstructed using sparse and noisymeasurements. Results indicate a good agreement between the applied and thepredicted dynamic load and can be extended to calculate global responses andthe resulting internal forces. Uses of the developed framework includevalidation of design models and assumptions, as well as prognosis of responsesto assist in damage detection and structural health monitoring.

A physics-informed machine learning model for reconstruction of dynamic loads