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Effects of bonding on the performance of optical fiber strain sensors
Author(s) -
Floris Ignazio,
Sangiorgio Valentino,
Adam José M.,
Uva Giuseppina,
Rapido Monica,
Calderón Pedro A.,
Madrigal Javier
Publication year - 2021
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2782
Subject(s) - structural health monitoring , optical fiber , finite element method , fiber , fiber optic sensor , point (geometry) , computer science , mechanical engineering , materials science , structural engineering , engineering , composite material , telecommunications , geometry , mathematics
Summary The structural health monitoring (SHM) of existing buildings, structures, and infrastructures has become increasingly important in recent years, while the interest of the scientific community is focused on the use of new high‐performance technologies. Fiber optic sensors have become particularly attractive, thanks to their potential for monitoring strain in smart structures. The performance of this new technology depends to a large extent on the bonding technique used for its manufacture. Although the related literature has identified a correlation between some efficiency issues and the geometrical parameters of the bonding and mechanical properties of the materials adopted, the phenomenon is still not completely understood. This paper describes an in‐depth study of the geometrical and mechanical parameters that influence the efficiency of optical fiber point sensors' surface bonding by synergistically related techniques such as computational simulation, experimental tests, sensor manufacturing, and data analysis. The paper's novelty is fourfold: (1) the investigation of the strain transfer mechanism of surface‐bonded fiber optic sensors by considering, for the first time, all the parameters influencing the phenomenon through a considerable number of finite element (FE) analyses (117 three‐dimensional FE models); (2) the development of a series of bonding efficiency predictive models; (3) the design of a specific laboratory test to validate the computational outcomes; and (4) the definition of useful guidelines for effective bonding manufacturing in order to maximize the performance of these sensors when acquiring monitoring data.