Please use this identifier to cite or link to this item:

TitleAn overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms
Author(s)Masciotta, Maria Giovanna
Barontini, Alberto
Ramos, Luís F.
Amado-Mendes, Paulo
Lourenço, Paulo B.
KeywordsStructural health monitoring
nature-inspired sensing paradigms
bio-inspired algorithms
bio-inspired SHM sensors
Issue date2019
JournalInternational Journal of Bio-Inspired Computation
Abstract(s)In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.
Publisher version
AccessOpen access
Appears in Collections:ISISE - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
authorFinalVersion.pdf1,04 MBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID