@article{1711,
  keywords = {mywork},
  author = {Simon Laflamme and Erik Blasch and Filippo Ubertini and Zheng Liu and John Wertz and Christine Knott and Matthew Cherry and Eric Lindgren and Fu-Kuo Chang and Amrita Kumar and Jack Poole and Keith Worden and Austin Downey and Jie Wei and Patrick Musgrave and Adrian Wong and Giuseppe Quaranta and Marco Rosso and Giuseppe Marano and Yu Chen and Erika Ardiles-Cruz and Mohammad Soleimani-Babakamali and Onur Avci and Daniel Inman and Ertugrul Taciroglu and Jacob Dodson and Genda Chen and Wei Meng and Chang Zhu and Zemin Liu and Jie Zuo and Quan Liu and Sadik Khan and Chao Hu and Zhen Hu and Alice Cicirello and Elizabeth Cross and Eleni Chatzi and Yang Weng and Jingyi Yuan and Song Wen and Ligong Han and Dimitris Metaxas and Eleonora Tronci and Babak Moaveni and Qian Chen and Ming Ng and J{\"u}rgen Hackl and Genshe Chen and Sixiao Wei and Stergios-Aristoteles Mitoulis and Ivan Izonin and Giuseppina Uva and Sergio Ruggieri and Zhu Mao and Serkan Kiranyaz and Ozer Devecioglu and Moncef Gabbouj and Javad Mohammadi},
  title = {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).},
  year = {2026},
  journal = {Measurement Science and Technology},
  volume = {37},
  number = {10},
  pages = {103001},
  publisher = {IOP Publishing},
  issn = {0957-0233},
  doi = {10.1088/1361-6501/ae3abb},
}