Green Physics-Informed Machine Learning Models For Structural Health Monitoring

TL;DR

This paper explores physics-informed machine learning models for structural health monitoring, emphasizing environmental impact reduction and computational efficiency while maintaining high performance.

Contribution

It compares black-box and grey-box models in terms of environmental impact and demonstrates how grey-box models can reduce runtimes and carbon emissions.

Findings

Grey-box models show high extrapolative performance.

Physics-informed models can reduce computational costs.

Reduced runtimes lead to lower carbon emissions.

Abstract

Machine learning continues to emerge as an important tool to be utilised within structural engineering and structural health monitoring, due to its ability to accurately and quickly perform both regression and classification tasks. However, a purely data driven approach has its limitations, particularly where we lack data from relevant environmental and operational conditions, a situation that has led to the development of physics-informed machine learners for structural health monitoring. These "grey-box" models take into account the physical insight that an engineer would have about the structure they are modelling and have shown promising results in the structural engineering field among many others. This work compares black and grey-box models through a "green" lens, comparing them in terms of their environmental impact, and investigating how the high extrapolative performance of grey-box models can reduce their runtimes and therefore carbon emissions. The authors aim to develop physics-informed models with reduced computational costs, while maintaining high performance, illustrated through a structural health monitoring case study.