Performance of embedded short-gage-length optical fiber sensors in a fatigue-loaded reinforced concrete specimen

Vries, Marten de; Nasta, Manish H.; Bhatia, Vikram; Tran, Tuan Anh; Greene, Jonathan A.; Claus, Richard O.; Masri, Sami F.

doi:10.1088/0964-1726/4/1a/013

Cited by 31 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…De Vires et al [7] reported that Fabry-Perot FOS output signals compared well with the output signals obtained from collocated strain gauge in the test of a concrete cross-beam specimen, and FOS had a much better signal-to-noise ratio than the strain gauge. Quirion and Ballivy [8,9] have evaluated the performance of the Fabry-Perot FOS when it was embedded in concrete cylinders.…”

Section: Monitoring Of Strain and Displacementmentioning

confidence: 93%

Smart Sensing Technologies for Structural Health Monitoring of Civil Engineering Structures

Sun

Staszewski

Swamy

2010

Advances in Civil Engineering

114

View full text Add to dashboard Cite

Structural Health Monitoring (SHM) aims to develop automated systems for the continuous monitoring, inspection, and damage detection of structures with minimum labour involvement. The first step to set up a SHM system is to incorporate a level of structural sensing capability that is reliable and possesses long term stability. Smart sensing technologies including the applications of fibre optic sensors, piezoelectric sensors, magnetostrictive sensors and self-diagnosing fibre reinforced composites, possess very important capabilities of monitoring various physical or chemical parameters related to the health and therefore, durable service life of structures. In particular, piezoelectric sensors and magnetorestrictive sensors can serve as both sensors and actuators, which make SHM to be an active monitoring system. Thus, smart sensing technologies are now currently available, and can be utilized to the SHM of civil engineering structures. In this paper, the application of smart materials/sensors for the SHM of civil engineering structures is critically reviewed. The major focus is on the evaluations of laboratory and field studies of smart materials/sensors in civil engineering structures.

show abstract

Section: Monitoring Of Strain and Displacementmentioning

confidence: 93%

Smart Sensing Technologies for Structural Health Monitoring of Civil Engineering Structures

Sun

Staszewski

Swamy

2010

Advances in Civil Engineering

114

View full text Add to dashboard Cite

show abstract

“…The round trip phase of the beam can be shown to be in the form of: nl 4 (1) where n in the index of refraction, is the optical wavelength, and l is the length of the cavity [14]. The ratio of reflected power to incident power is related to the round trip phase difference and the reflecting coefficient: (2) where R 1 and R 2 are the power reflection coefficients, and A is the cavity losses [10]. The strain on the FPI as a result of external parameters is given by:…”

Section: Efpi Sensor and Sinusoidal Strainmentioning

confidence: 99%

“…At low strain levels, the Fabry-Perot strain sensors have a linear strain to output intensity ratio. However, at high strain levels, they are very non-linear, requiring special demodulation schemes such as fringe counting, phase counting, and white-light interferometery [9,10,11,12]. In this work, a method employing the harmonics of an Extrinsic Fabry-Perot Interferometric (EFPI) signal as calibration points will be described.…”

Section: Introductionmentioning

confidence: 99%

Four-point calibration of PVDF sensor using extrinsic Fabry-Perot interferometric sensors

Abdi

Watkins

2003

SPIE Proceedings

View full text Add to dashboard Cite

The performance of sensor systems in smart structures is subject to orientation, installation, and environmental effects. The capability for initial and maintenance calibrations can provide added confidence in sensor information and facilitate investigations of long-term sensor behavior. The use of Extrinsic Fabry-Perot Interferometric (EFPI) fiber-optic sensors was examined for calibration of companion strain sensors. The output of the EFPI sensor is periodic with strain and the signal behavior with periodic strain displays well-defined harmonic content. In particular, the strains giving maximums and minimums in the harmonics can be calculated from the excitation wavelength and the EFPI gage length. A Polyvinylidene Fluoride (PVDF) piezoelectric strain sensor was surface mounted on a cantilever beam and its voltageto-strain ratio was precisely calibrated using the accuracy of a co-located EFPI sensor. The experimental responses of both sensors were obtained for a periodic actuation. The PVDF output was calibrated using a linear-fit of the strains obtained from four points in the harmonic response of the reference EFPI sensor. The selected maximum and minimum points of the EFPI harmonics were directly observed with a spectrum analyzer. This fast, efficient approach was performed under resonant conditions with relatively inexpensive demodulation requirements. The results of the calibration compared well with the expected PVDF response.

show abstract

“…Recent employment of a fiber optic sensors included embedding fiber optic sensors into concrete superstructure for civil application to measure vibration intensities, wind pressure, temperature, stress/strain sensing, and general building health monitoring [8,9]. Another application includes using fiber optic sensors on railway bridge and scale model bridge element [8,10,11].…”

Section: Introductionmentioning

confidence: 99%

Modal analysis using the Bessel harmonics of an extrinsic Fabry-Perot interferometric sensor (EFPI) and neural networks

Abdi

Watkins

2006

SPIE Proceedings

View full text Add to dashboard Cite

A demodulation system employing neural networks is used to process the non-linear signal from an extrinsic FabryPerot interferometric (EFPI) sensor. A sinusoidal strain is theoretically shown to produce well-defined Bessel harmonics in the EFPI signal. The neural network demodulator (NND) uses a Fourier Series Neural Network to separate the Bessel harmonic components of the EFPI signal and a Back-Propagation Neural Network is used to predict the strain levels through the analysis of the Bessel harmonics. The NND is first simulated in a computer program and then actually employed in an experimental setting to determine the frequency response of a 25 cm composite cantilever beam. A function generator was used to drive a PZT actuator attached to the composite beam and the resulting periodic strain was measured by the EFPI; the frequency of the composite beam was varied between 10 Hz and 900 Hz. The NND demodulated the EFPI signal and determined the frequency response of the composite beam. The results show that the NND accurately reproduced the natural frequencies and mode shapes of the cantilever beam.

show abstract

Performance of embedded short-gage-length optical fiber sensors in a fatigue-loaded reinforced concrete specimen

Cited by 31 publications

References 6 publications

Smart Sensing Technologies for Structural Health Monitoring of Civil Engineering Structures

Smart Sensing Technologies for Structural Health Monitoring of Civil Engineering Structures

Four-point calibration of PVDF sensor using extrinsic Fabry-Perot interferometric sensors

Modal analysis using the Bessel harmonics of an extrinsic Fabry-Perot interferometric sensor (EFPI) and neural networks

Contact Info

Product

Resources

About