• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.700-709
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• 2009

FBG sensor system is applied to the concrete lining structure in Taegu subway. Near the structure, the power cable tunnel construction started. We wanted to measure the deformation of the structure due to the construction by the FBG sensor. The applied sensor has the gauge length of 1 meter to overcome the inhomogeneity of the concrete material with enough length. In order to fix tightly to the structure, the partially stripped parts of the sensor glued to the package and slip phenomenon between fiber and acrylate jacket was prevented. Prestrain of the sensor was imposed by controlling the two fixed points with bolts and nuts in order to measure compressive strain as well as tensile strain. The behavior of subway lining structure could be monitored very well.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2004.04a
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• pp.115-118
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• 2004

In this paper, a Fiber Bragg Grating (FBG) sensor system for smart structures is described. FBGs are well-suited for long term and extremely severe experiments, where traditional strain gauges fail. In the system, a reflect wave-length measurement method which employs a tunable light source to find out the center wave-length of FBG sensor is used. We applied the FBG system to composite repairing structures and beam column joint of building structure. We also applied the system to nuclear energy power plant for structural integrity test to measure the displacement of the structure under designed pressure and to check the elasticity of the structure by measuring the residual strain. The system works very well and it is expected that it can be used for a real-time strain, temperature and vibration detectors as parts of smart structures.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.350-351
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• 2006

Thick film mechanical sensors can be categorized into four main areas piezoresistive, piezoelectric, capacitive and mechanic tube. In this areas, the thick film strain gage is the earliest example of a primary sensing element based on the substrates. The latest thick film sensor is used various pastes that have been specifically developed for pressure sensor application. Some elastic materials exhibit a change in bulk resistivity when they are subjected to displacement by an applied pressure. This property is referred to as piezoresistivity and is a major factor influencing the sensitivity of a piezoresistive strain gage. The effect of thick film resistors was first noticed in the early 1970, as described by Holmes in his paper in 1973.

• Journal of Sensor Science and Technology
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• v.14 no.1
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• pp.56-61
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• 2005

This paper present characteristics of chromium oxide thin-film as piezoresistive sensors, which were deposited on Si substrates by DC reactive magnetron sputtering in an argon-Oxide atmosphere for high temperature applications. The chemical composition, physical and electrical properties and thermal stability ranges of the $CrO_{x}$ sensing elements have studied. $CrO_{x}$ thin films with a linear gauge factor(GF${\fallingdotseq}$15), high electrical resistivity (${\rho}$ = $340{\mu}{\Omega}cm$) and TCR<-55 ppm/$^{\circ}C$ have been obtained. These $CrO_{x}$ thin films may allow high temperature pressure sensor miniaturization to be achieved.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.2
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• pp.59-66
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• 2002

In this paper, we present the simulataneous monitoring of the strain and temperature during cures f various composite laminates using fiber Bragg grating/extrinsic Fabry-Perot interferometric (FBG/EFPI) hybrid sensors. Three types of graphite/epoxy composite were used : a undirectional laminate, a symmetric cross-ply laminate, and a fabric laminate. Two FBG/EFPI hybrid sensors were embedded in each laminate at different directions and different locations. We performed the real time monitoring of fabrication strains and temperatures at two points within the composite laminates during cure process in an autoclave. Throuhg these experiments, FBG/EFPI sensors proved to be an efficient choice for smart cure monitoring of composite structures.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.1
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• pp.23-32
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• 1998

A shape estimation is needed to control actively a smart structure. A method is, hence, proposed to predict the deformed shape of the structure subjected to unknown external load using the signal from sensors attached to the structure. The shape estimation is based on the relationship between the deformation of the structure and the signal from the sensors. The matrix containing the relationship between the deformation and signal is obtained using fictitious force or eigenvector of global stiffness matrix. Then the deformed shape can be predicted using the linear matrix and signal from sensors attached to the structure. To verify this method, experiment and FEM were performed and it was shown that the shape estimation method based on the fictitious force predicts deflections well and more accurately than that based on eigenvector.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.2
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• pp.221-228
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• 2012

An active compensation method for the deformation of composite structures using additional controllable metal parts is proposed, and its feasibility is experimentally investigated in a simulated space environment. Composite specimens are tested in a vacuum chamber, which is able to maintain pressure on the order of 10-3 torr and interior temperature in the range of ${\pm}30^{\circ}C$. The displacement-measuring interferometer system, which consists of a heterodyne HeNe laser and an interferometer, is used to measure the displacement of the whole structure. Meanwhile, the strain of the composite part and temperature of both parts are measured by fiber Bragg grating sensors and thermistors, respectively. The displacement of the composite structure is maintained within a tolerance of ${\pm}1{\mu}m$ by controlling the elongation of the metal part, which is bonded to the end of the composite part. Also, the possibility of fiber Bragg grating sensors as control input sensors is successfully demonstrated using a proper corrective factor based on the specimen temperature gradient data.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.347-356
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• 2004

In order to extend the lifetime of buildings and civil infrastructure, patch type fibrous composite retrofitting materials are widely used. Retrofitted concrete columns and beams gain stiffness and strength, but lose toughness and show brittle failure. Usually, the cracks in concrete structures are visible to the naked eye and the status of the structure in the life cycle is estimated through visual inspections. After retrofitting of the structure, crack visibility is blocked by retrofitted composite materials. Therefore, structural monitoring after retrofitting is indispensable and self diagnosis method with optical fiber sensors is very useful. In this paper, we try to detect the peel out effect and find the strain difference between the main structure and retrofitting patch material when they separate from each other. In the experiment, two fiber optic Bragg grating sensors are applied to the main concrete structure and the patching material separately at the same position. The sensors show coincident behaviors at the initial loading, but different behaviors after a certain load. The test results show the possibility of optical fiber sensor monitoring of beam structures retrofitted by the composite patches.

• Smart Structures and Systems
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• v.5 no.4
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• pp.381-395
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• 2009

The paper presents a case study in which the structural condition assessment of the East Bay bridge in Gibsonton, Florida is evaluated with the help of remote health monitoring techniques. The bridge is a four-span, continuous, deck-type reinforced concrete structure supported on prestressed pile bents, and is instrumented with smart Fiber Optic Sensors. The sensors used for remote health monitoring are the newly emerged Fabry-Perot (FP), and are both surface-mounted and embedded in the deck. The sensing system can be accessed remotely through fast Digital Subscriber Lines (DSL), which permits the evaluation of the bridge behavior under live traffic loads. The bridge was open to traffic since March 2005, and the collected structural data have been continuously analyzed since. The data revealed an increase in strain readings, which suggests a progression in damage. Recent visual observations also indicated the presence of longitudinal cracks along the bridge length. After the formation of these cracks, the sensors readings were analyzed and used to extrapolate the values of the maximum stresses at the crack location. The data obtained were also compared to initial design values of the bridge under factored gravity and live loads. The study showed that the proposed structural health monitoring technique proved to provide an efficient mean for condition assessment of bridge structures providing it is implemented and analyzed with care.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.814-822
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• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.