The synthesis of data from instrumented structures and physics-based models via Gaussian processes

Abstract

A recent development which is poised to disrupt current structural engineering practice is the use of data obtained from physical structures such as bridges, viaducts and buildings. These data can represent how the structure responds to various stimuli over time when in operation, providing engineers with a unique insight into how their designs are performing. With the advent of advanced sensing technologies and the Internet of Things, the efficient interpretation of structural health monitoring data has become a big data challenge. Many models have been proposed in literature to represent such data, such as linear statistical models. Based upon these models, the health of the structure is reasoned about, e.g. through damage indices, changes in likelihood and statistical parameter estimates. On the other hand, physics-based models are typically used when designing structures to predict how the structure will respond to operational stimuli. What remains unclear in the literature is how to combine the observed data with information from the idealised physics-based model into a model that describes the responses of the operational structure. This paper introduces a new approach which fuses together observed data from a physical structure during operation and information from a mathematical model. The observed data are combined with data simulated from the physics-based model using a multi-output Gaussian process formulation. The novelty of this method is how the information from observed data and the physics-based model is balanced to obtain a representative model of the structures response to stimuli. We present our method using data obtained from a fibre-optic sensor network installed on experimental railway sleepers. We discuss how this approach can be used to reason about changes in the structures behaviour over time using simulations and experimental data.

References in corpus (4)

• Machine Learning of Linear Differential Equations using Gaussian Processes
• A Bayesian Framework for Assessing the Strength Distribution of Composite Structures with Random Defects
• Multilevel Monte Carlo for uncertainty quantification in structural engineering
• A Quantile-Based Approach to Modelling Recovery Time in Structural Health Monitoring

Cited by in corpus (4)

• The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions
• Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes
• Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
• Physics-informed Gaussian process model for Euler-Bernoulli beam elements