Physics-informed machine learning for Structural Health Monitoring

Abstract

The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit. This chapter introduces the concept of physics-informed machine learning, where one adapts ML algorithms to account for the physical insight an engineer will often have of the structure they are attempting to model or assess. The chapter will demonstrate how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability in an SHM setting. A particular strength of the approach demonstrated here is the capacity of the models to generalise, with enhanced predictive capability in different regimes. This is a key issue when life-time assessment is a requirement, or when monitoring data do not span the operational conditions a structure will undergo. The chapter will provide an overview of physics-informed ML, introducing a number of new approaches for grey-box modelling in a Bayesian setting. The main ML tool discussed will be Gaussian process regression, we will demonstrate how physical assumptions/models can be incorporated through constraints, through the mean function and kernel design, and finally in a state-space setting. A range of SHM applications will be demonstrated, from loads monitoring tasks for off-shore and aerospace structures, through to performance monitoring for long-span bridges.

References in corpus (14)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
• Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
• Deep Learning of Vortex Induced Vibrations
• Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
• Gaussian Process Kernels for Pattern Discovery and Extrapolation
• Hilbert Space Methods for Reduced-Rank Gaussian Process Regression
• Considering discrepancy when calibrating a mechanistic electrophysiology model
• Probabilistic modelling and reconstruction of strain
• Grey-box models for wave loading prediction
• Grey-box state-space identification of nonlinear mechanical vibrations
• Structured Machine Learning Tools for Modelling Characteristics of Guided Waves
• Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data
• Sequential Inference for Latent Force Models

Cited by in corpus (2)

• Towards a population-informed approach to the definition of data-driven models for structural dynamics
• Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder