Green Physics-Informed Machine Learning Models For Structural Health Monitoring
For structural engineers, this work highlights the environmental benefits of physics-informed models over purely data-driven approaches, though the results are incremental and case-study-specific.
This work compares black-box and physics-informed (grey-box) machine learning models for structural health monitoring, focusing on their environmental impact. The authors show that grey-box models reduce computational costs and carbon emissions while maintaining high performance, demonstrated through a case study.
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.