Roadmap on measurement technologies for next generation structural health monitoring systems

Laflamme, Simon and Ubertini, Filippo and Di Matteo, Alberto and Pirrotta, Antonina and Perry, Marcus and Fu, Yuguang and Li, Jian and Wang, Hao and Hoang, Tu and Glisic, Branko and Bond, Leonard J and Pereira, Mauricio and Shu, Yening and Loh, Kenneth J and Wang, Yang and Ding, Siqi and Wang, Xinyue and Yu, Xun and Han, Baoguo and Goldfeld, Yiska and Ryu, Donghyeon and Napolitano, Rebecca and Moreu, Fernando and Giardina, Giorgia and Milillo, Pietro (2023) Roadmap on measurement technologies for next generation structural health monitoring systems. Measurement Science and Technology, 34 (9). 093001. ISSN 0957-0233 (https://doi.org/10.1088/1361-6501/acd135)

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Abstract

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots.

ORCID iDs

Laflamme, Simon, Ubertini, Filippo, Di Matteo, Alberto, Pirrotta, Antonina, Perry, Marcus ORCID logoORCID: https://orcid.org/0000-0001-9173-8198, Fu, Yuguang, Li, Jian, Wang, Hao, Hoang, Tu, Glisic, Branko, Bond, Leonard J, Pereira, Mauricio, Shu, Yening, Loh, Kenneth J, Wang, Yang, Ding, Siqi, Wang, Xinyue, Yu, Xun, Han, Baoguo, Goldfeld, Yiska, Ryu, Donghyeon, Napolitano, Rebecca, Moreu, Fernando, Giardina, Giorgia and Milillo, Pietro;
CORE (COnnecting REpositories)