• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.165-171
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• 1992

진동하는 구조물을 설계할 때에는 그 구조물 중의 Strain이나 응력이 최대가 되는 장소나 시각을 알 필요가 있다. 지금까지의 Strain 해석에는 Strain gauge 등과 같은 접촉법이 많이 이용되고 있다. 더우기, 접촉법으로 대변형 진동을 하는 물체의 Strain을 해석하는 것은 곤란하다. 최근에는 비접촉법으로 Strain 분포를 해석하기 위해 화상처리를 이용한 계측이 행하여지고 있다. 이들의 Strain 분포를 측정하는 광학적인 방법으로는 격자법, Moire법, 홀로 그랩픽 간섭법 등이 있다. 특히 대변형이나 대Strain을 해석하는 데에는 격자법이 많이 이용되고 있는데, 종래의 격자법은 Data를 처리하는 데에 많은 시간과 노력이 소요되고 작업도 매우 복잡하며, Data의 수도 제한이 되어서 구조물의 분포의 해석 정도에 큰 영향을 미치게 된다. 본 논문 에서는 스테레오법을 이용해서 2차원 격자를 붙인 시료표면의 각 점의 3차원 좌표를 계측하고, 또 Fourier 변환 격자법을 적용하여 촬영된 2차원 격자의 화상에서 위상치를 구한다. 그리고 물체의 변형 전후의 대응 관계의 화상에서 3차원 형상과 Strain 분포를 해석하는 방법을 제안한다. 이 방법을 이용하면 진동하는 구조 물의 3차원 변위분포, Strain 분포를 정도 좋게 해석할 수가 있다.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2386-2394
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• 2022

In this paper, smooth and notched bar tensile tests of austenitic stainless steel 304 are performed, covering four different multi-axial stress states and six different strain rate conditions, to investigate the effect of the stress triaxiality and strain rate on fracture strain. Test data show that the measured true fracture strain tends to decrease with increasing stress triaxiality and strain rate. The test data are then quantified using the Johnson-Cook (J-C) fracture strain model incorporating combined effects of the stress triaxiality and strain rate. The determined J-C model can predict true fracture strain overall conservatively with the difference less than 20%. The conservatism in the strain-based acceptance criteria in ASME B&PV Code, Section III, Appendix FF is also discussed.

• Smart Structures and Systems
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• v.29 no.4
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• pp.599-616
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• 2022

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3E
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• pp.84-94
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• 2008

Displacement estimation is a crucial step in ultrasonic strain imaging. The displacement between a pre- and postcompression signal in the current data window is estimated by first shifting the postcompression signal by the displacement obtained in the previous data window to reduce their decorrelation and then determining the remaining part of the displacement through autocorrelation and conversion of phase difference into time delay. However, since strain image quality tends to vary with the amount of compression applied, we propose two new methods for enhancing strain image quality, i.e., displacement normalization and adaptive persistence. Both in vitro and in vivo experiments are carried out to acquire ultrasound data and produce strain images in real time under the application of quasi static compression. The experimental results demonstrate that the methods are quite effective in improving strain image quality and thus can be applied to implementing an ultrasound elasticity imaging system that operates in real time.

• Journal of the Korea institute for structural maintenance and inspection
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• v.4 no.2
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• pp.103-111
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• 2000

The objectives of the study are to know the strain distribution and modal dynamic behaviour of steel bridge girders by actual traffic load. The live load effect depends on many parameters including the span length, gross vehicle weight, axle weight, axle configuration so on. For the estimation of static and dynamic characteristic, strain data caused by moving loads and traffic characteristics of passing vehicle under actual traffic load have measured using Bridge Weigh in Motion. To confirm the reliability of BWIM system, strain data measured using the $120{\Omega}$ strain gauge under the same condition. It is considered that the data acquired from BWIM system have reliability through the analysis and comparison between stress measured by strain data from BWIM and computed by FEM. Additionally according to the measured strain data of up-line and down-line on the highway, the up-line bridge grows more faster than the down-line bridge and girder 4 and 5 carry more load when vehicles pass the inner line and girder 2 and 3 does when vehicles pass the outer line as this case(the bridge composed with 5 girders).

• Journal of KIBIM
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• v.10 no.2
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• pp.21-28
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• 2020

As the number of aging bridges increases, more studies are being conducted on developing effective and reliable methods for the assessment and maintenance of bridges. With the advancement in new sensing systems and data learning techniques through AI technology, there is growing interests in how to evaluate bridges using these advanced techniques. This paper presents a CNN(Convolution Neural Network) deep learning based technique for evaluating the damage existence and for estimating the damage location in PSC bridges using static strain data. Simulation studies were conducted to investigate the proposed method with error analysis. Damage was simulated as the reduction in the stiffness of a finite element. A data learning model was constructed by applying the CNN technique as a type of deep learning. The damage status and its location were estimated using data set built through simulation. It was assumed that the strain gauges were installed in a regular interval under the PSC bridge girders. In order to increase the accuracy in evaluating damage, the squared error between the intact and measured strains are computed and applied for training the data model. Considering the damage occurring near the supports, the results of error analysis were compared according to whether strain data near the supports were included.

• Computers and Concrete
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• v.11 no.2
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• pp.149-167
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• 2013

The complete stress-strain behavior of steel fiber reinforced concrete in compression is needed for the analysis and design of structures. An experimental investigation was carried out to generate the complete stress-strain curve of high-performance steel fiber reinforced concrete (HPSFRC) with a strength range of 52-80 MPa. The variation in concrete strength was achieved by varying the water-to-cementitious materials ratio of 0.40-0.25 and steel fiber content (Vf = 0.5, 1.0 and 1.5% with l/d = 80 and 55) in terms of fiber reinforcing parameter, at 10% silica fume replacement. The effects of these parameters on the shape of stress-strain curves are presented. Based on the test data, a simple model is proposed to generate the complete stress-strain relationship for HPSFRC. The proposed model has been found to give good correlation with the stress-strain curves generated experimentally. Inclusion of fibers into HPC improved the ductility considerably. Equations to quantify the effect of fibers on compressive strength, strain at peak stress and toughness of concrete in terms of fiber reinforcing index are also proposed, which predicted the test data quite accurately. Compressive strength prediction model was validated with the strength data of earlier researchers with an absolute variation of 2.1%.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.461-470
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• 2007

Parameter identification is studied in viscoelastic rods by solving an inverse problem numerically. The material properties of the rod, which appear in the constitutive relations, are recovered by optimizing an objective function constructed from reference strain data. The resulting inverse algorithm consists of an optimization algorithm coupled with a corresponding direct algorithm that computes the strain fields given a set of material properties. Numerical results are presented for two model inverse problems; (i)the effect of noise in the reference strain fields (ii) the effect of minimal reference data in space and/or time data.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1616-1629
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• 2023

To quantify the conservatism of existing ASME strain-based evaluation methods for seismic loading, this paper presents very low cycle fatigue test data of elbows under various cyclic loading conditions and comparison of evaluation results with experimental failure cycles. For strain-based evaluation methods, the method presented in ASME BPVC CC N-900 and Sec. VIII are used. Predicted failure cycles are compared with experimental failure cycle to quantify the conservatism of evaluation methods. All methods give very conservative failure cycles. The CC N-900 method is the most conservative and prediction results are only ~0.5% of experimental data. For Sec. VIII method, the use of the option using code tensile properties gives ~3% of experimental data, and the use of the material-specific reduction of area can reduce conservatism but still gives ~15% of experimental data.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4030-4048
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• 2022

In this study, road transportation tests were conducted with surrogate fuel assemblies under normal conditions of transport to evaluate the vibration and shock load characteristics of spent nuclear fuel (SNF). The overall test data analysis was conducted based on the measured acceleration and strain data obtained from the speed bump, lane-change, deceleration, obstacle avoidance, and circular tests. Furthermore, representative shock response spectrums and power spectral densities of each test mode were acquired. Amplification or attenuation characteristics were investigated according to the load transfer path. The load attenuated significantly as it transferred from the trailer to the cask. By contrast, the load amplified as it transferred from the cask to the surrogate SNF assembly. The fuel loading location on the cask disk assembly did not exhibit a significant influence on the strain measured from the fuel rods. The principal strain was in the vertical direction, and relatively large strain values were obtained in spans with large spacing between spacer grids. The influence of the lateral location of fuel rods was also investigated. The fuel rods located at the side exhibited relatively large strain values than those located at the center. Based on the strain data obtained from the test results, a hypothetical road transportation scenario was established. A fatigue evaluation of the SNF rod was performed based on this scenario. The evaluation results indicate that no fatigue damage occurred on the fuel rods.