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

TL;DR

This paper introduces a probabilistic physics-informed machine learning framework using Gaussian process regression to reconstruct dynamic loads on long-span bridges from incomplete and noisy measurement data, aiding structural health monitoring.

Contribution

It presents a novel framework that combines physics-based modeling with machine learning to accurately estimate dynamic forces despite data uncertainties.

Findings

Good agreement between predicted and actual dynamic loads.

Framework effectively handles incomplete and noisy data.

Potential for use in damage detection and structural health monitoring.

Abstract

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.