Roadmap: Integrating artificial intelligence in structural health monitoring systems

Abstract Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to solving hard engineering problems, including that of structural health monitoring (SHM). SHM consists of automating the condition assessment task of civil, health, mechanical, and aerospace systems using measurements obtained from temporary or permanently installed sensors. Often, the systems of interest are geometrically large and/or technically complex, which complicates the development and application of physics-based methods. It follows that AI is seen as a key potential contributor enabling SHM in field applications for data-driven analysis. As with many research endeavors, many concepts using AI for SHM have been explored in the literature. Nevertheless, very few AI methods have been deployed in the context of SHM, which may be due to the lack of available data supporting their capabilities, limited integrated AI-SHM systems capable of providing results to users and operators with decision-making capabilities, or certification of AI methods for safety-critical applications. The objective of this Roadmap publication is to discuss the integration of AI at the system level enabling SHM, including associated challenges and opportunities such as those found in common metrics of concern (e.g. transparency, interpretability, explainability, security, certifiability, etc), with a particular focus on providing a path to research and development efforts that could yield impactful field applications. The overview of available methods and directions will provide the readers with applicability of AI for certain SHM designs (software), availability of common data sets for further AI comparisons (data), and lessons learned in implementation (hardware).

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Publication Details

Journal
Measurement Science and Technology
Published
2026-01-20
DOI
https://doi.org/10.1088/1361-6501/ae3abb
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Roadmap: Integrating artificial intelligence in structural health monitoring systems

Özer Can Devecioğlu, Elizabeth J. Cross, Ivan Izonin, Simon Laflamme et al.
Measurement Science and Technology
Structural Health Monitoring Techniques
article

Roadmap: Integrating artificial intelligence in structural health monitoring systems

Özer Can Devecioğlu, Elizabeth J. Cross, Ivan Izonin, Simon Laflamme, Jie Zuo, Ertuǧrul Taciroğlu, Patrick Musgrave, Zhu Mao, Sergio Ruggieri, Erika Ardiles-Cruz, Jacob Dodson, John Wertz, Marco Martino Rosso, Eleonora M. Tronci, Genda Chen, Matthew Cherry, Babak Moaveni, Sixiao Wei, Jürgen Hackl, Javad Mohammadi, Stergios-Aristoteles Mitoulis, Keith Worden, Fu-Kuo Chang, Jingyi Yuan, Onur Avci, Serkan Kiranyaz, Giuseppina Uva, Zheng Liu, Alice Cicirello, Wei Meng, Quan Liu, Mohammad Hesam Soleimani-Babakamali, Zhen Hu, Giuseppe Quaranta, Adrian Wong, Eleni Chatzi, Giuseppe Carlo Marano, Yang Weng, Jie Wei, Sadik Khan, Christine Knott, Zemin Liu, Song Wen, Amrita Kumar Kumar, Chao Hu, Daniel J Inman, Qian Chen, Erik Blasch, Austin Downey, Jack Poole, Chang Zhu, Moncef Gabbouj, Filippo Ubertini, Genshe Chen, Ligong Han, Yu Chen, Eric A. Lindgren, Ming Shan Ng
article en

Abstract

Abstract Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to solving hard engineering problems, including that of structural health monitoring (SHM). SHM consists of automating the condition assessment task of civil, health, mechanical, and aerospace systems using measurements obtained from temporary or permanently installed sensors. Often, the systems of interest are geometrically large and/or technically complex, which complicates the development and application of physics-based methods. It follows that AI is seen as a key potential contributor enabling SHM in field applications for data-driven analysis. As with many research endeavors, many concepts using AI for SHM have been explored in the literature. Nevertheless, very few AI methods have been deployed in the context of SHM, which may be due to the lack of available data supporting their capabilities, limited integrated AI-SHM systems capable of providing results to users and operators with decision-making capabilities, or certification of AI methods for safety-critical applications. The objective of this Roadmap publication is to discuss the integration of AI at the system level enabling SHM, including associated challenges and opportunities such as those found in common metrics of concern (e.g. transparency, interpretability, explainability, security, certifiability, etc), with a particular focus on providing a path to research and development efforts that could yield impactful field applications. The overview of available methods and directions will provide the readers with applicability of AI for certain SHM designs (software), availability of common data sets for further AI comparisons (data), and lessons learned in implementation (hardware).

Measurement Science and TechnologyVol. 37(10)
Rutgers, The State University of New Jersey (US), Worcester Polytechnic Institute (US), West Virginia University (US), Tufts University (US), Binghamton University (US), City College of New York (US), United States Air Force Research Laboratory (US), University of Connecticut (US), University of British Columbia (CA), University of South Carolina (US), University of California, Los Angeles (US), Iowa State University (US), Politecnico di Torino (IT), Wuhan University of Technology (CN), Princeton University (US), Missouri University of Science and Technology (US), Kyoto Institute of Technology (JP), University of Perugia (IT), University of Michigan (US), Wright-Patterson Air Force Base (US), Kelowna General Hospital (CA), Eglin Air Force Base (US), University of Florida (US), ETH Zurich (CH), Rutgers Sexual and Reproductive Health and Rights (NL), Bridge University (SS), University of Michigan–Dearborn (US), Intelligent Fusion Technology (United States) (US), Acellent Technologies (United States) (US), Arizona State University (US), Qatar University (QA), Jackson State University (US), Polytechnic University of Bari (IT), University of Birmingham (GB), Okanagan University College (CA), Sapienza University of Rome (IT), The University of Texas at Austin (US), University of Sheffield (GB), Northeastern University (CN), Stanford University (US)
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Openalex Percentile: Top 82%
Structural Health Monitoring Techniques
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