Optical Sensors for Structural Health Monitoring

The evolution and need for the preservation and maintenance of existing structures, recent or historical, has fostered research in the area of structural monitoring, translated into the development of new techniques, equipment and sensors. Early detection of damage and accurate assessment of structu...

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Bibliographic Details
Other Authors: Antunes, Paulo (Editor), Varum, Humberto (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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DOAB: description of the publication
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520 |a The evolution and need for the preservation and maintenance of existing structures, recent or historical, has fostered research in the area of structural monitoring, translated into the development of new techniques, equipment and sensors. Early detection of damage and accurate assessment of structural safety requires monitoring systems, the data from which can be used to calibrate numerical models for structural analysis and to assess safety. Data are obtained under real-time conditions, considering a group of parameters related to structural properties, such as stresses, accelerations, deformations and displacements. The analysis of structural properties is particularly relevant when the structure is subjected to extreme events (earthquakes, wind, fire and explosions, among others) or repeated loads (road/rail/air traffic, vibrations induced by equipment and machines), since they affect the structural integrity and put the users at risk. In order to prevent the severe damage and eventual collapse of structures, and consequent human, material and economic losses, the implementation of monitoring systems becomes a valuable tool for today's society. Monitoring of structures is becoming increasingly important, not only as preventive action, but also due to actual economic and sustainability concerns, to ensure a safer and more comfortable built environment. 
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650 7 |a History of engineering & technology  |2 bicssc 
653 |a image-based measurement 
653 |a crack measurement 
653 |a shear cracks 
653 |a flexural cracks 
653 |a damage index 
653 |a nuclear power plant 
653 |a visual inspection 
653 |a photometric stereo 
653 |a 3D reconstruction 
653 |a rotating stall 
653 |a non-synchronous blade vibration 
653 |a blade tip timing 
653 |a centrifugal compressor 
653 |a distributed measurements 
653 |a fiber optic sensors 
653 |a scour 
653 |a soil-structure interaction 
653 |a winkler model 
653 |a equivalent length 
653 |a corrosion sensor 
653 |a oil and gas pipelines 
653 |a optical fibers 
653 |a Fiber Bragg Grating (FBG) 
653 |a distributed optical fiber strain sensing cable 
653 |a Brillouin scattering 
653 |a Rayleigh scattering 
653 |a strain sensing cable characterization 
653 |a elasto-plastic behavior 
653 |a strain sensitivity coefficients 
653 |a bridge damage detection 
653 |a fiber optic gyroscope 
653 |a deep learning 
653 |a convolutional neural network 
653 |a Fiber Bragg grating 
653 |a fiber optic sensors embedded in concrete 
653 |a strain measurement 
653 |a monitoring 
653 |a cracking 
653 |a weldable fiber optic sensors 
653 |a optical fiber sensors 
653 |a material extrusion 
653 |a hybrid processes 
653 |a temperature and strain monitoring 
653 |a similarity measure 
653 |a subway tunnel 
653 |a distributed vibration 
653 |a feature extraction 
653 |a autoencoder 
653 |a ultra-weak FBG 
653 |a hyperspectral imaging 
653 |a spectral indices 
653 |a random forest 
653 |a growth stage 
653 |a Fusarium head blight 
653 |a structural health monitoring 
653 |a load localization 
653 |a load estimation 
653 |a depth sensor 
653 |a artificial neural networks 
653 |a castigliano's theorem 
653 |a crack detection 
653 |a crack opening 
653 |a distributed fiber optic sensors 
653 |a DIC 
653 |a UHPFRC 
653 |a testing 
653 |a SHM 
653 |a microcracking 
653 |a PAD 
653 |a environmental monitoring 
653 |a colorimetric detection 
653 |a water 
653 |a atmosphere 
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