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