Green Physics-Informed Machine Learning Models For Structural Health Monitoring

arXiv cs.LG / 5/1/2026

📰 NewsSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper presents “grey-box” physics-informed machine learning models for structural health monitoring, addressing limitations of purely data-driven approaches when relevant operating/environmental data are scarce.
It compares black-box and grey-box models with a “green” perspective, evaluating how environmental impact relates to model runtime and computational needs.
The authors argue that grey-box models’ stronger extrapolation performance can improve runtime efficiency, potentially lowering carbon emissions.
The study targets physics-informed modeling that keeps high predictive performance while reducing computational cost, demonstrated via a structural health monitoring case study.

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.