2020 article

Machine Learning for Structural Health Monitoring: Challenges and Opportunities

SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2020, Vol. 11379.

By: F. Yuan n, S. Zargar n, Q. Chen  n & S. Wang n

author keywords: Machine learning; artificial neural networks; physics-informed learning; visual inspection; augmented reality; impact diagnosis; damage diagnosis; structural health monitoring
TL;DR: As a step towards the goal of automated damage detection, preliminary results are presented from dynamic modelling of beam structures using physics-informed artificial neural networks and a sensing paradigm for non-contact full-field measurements for damage diagnosis is presented. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: December 11, 2020

A physics-based approach to structural health monitoring (SHM) has practical shortcomings which restrict its suitability to simple structures under well controlled environments. With the advances in information and sensing technology (sensors and sensor networks), it has become feasible to monitor large/diverse number of parameters in complex real-world structures either continuously or intermittently by employing large in-situ (wireless) sensor networks. The availability of this historical data has engendered a lot of interest in a data-driven approach as a natural and more viable option for realizing the goal of SHM in such structures. However, the lack of sensor data corresponding to different damage scenarios continues to remain a challenge. Most of the supervised machine-learning/deep-learning techniques, when trained using this inherently limited data, lack robustness and generalizability. Physics-informed learning, which involves the integration of domain knowledge into the learning process, is presented here as a potential remedy to this challenge. As a step towards the goal of automated damage detection (mathematically an inverse problem), preliminary results are presented from dynamic modelling of beam structures using physics-informed artificial neural networks. Forward and inverse problems involving partial differential equations are solved and comparisons reveal a clear superiority of physics-informed approach over one that is purely datadriven vis-à-vis overfitting/generalization. Other ways of incorporating domain knowledge into the machine learning pipeline are then presented through case-studies on various aspects of NDI/SHM (visual inspection, impact diagnosis). Lastly, as the final attribute of an optimal SHM approach, a sensing paradigm for non-contact full-field measurements for damage diagnosis is presented.