• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.170-180
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• 2008

Successful design, construction and maintenance of NATM tunnel demands prediction, control, stability guidelines, the estimation pre-improvement support system and monitoring of surface settlement, gradient and ground displacement with high accuracy. Moreover, urban NATM tunnel under difficult geotechnical conditions is important the estimation and necessary of pre-improvement support system. Various strategies have been proposed for the quantitative estimation of pre-improvement support system. This paper was investigated and analysed an assessment technique for the quantitative estimation of pre-improvement support system on underground space, as mountain and urban tunnel, in detail. The analysis performed on design and construction stage with field database using the proposed stability estimation index by many researcher including the critical strain and the apparent Young's modulus concept.

• 대한토목학회논문집
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• 제35권1호
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• pp.1-8
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• 2015

신재생 에너지 개발에 대한 사회적 요구가 증가하면서 경제성 및 기술 성숙도가 높은 풍력발전이 많은 관심을 받고 있다. 대규모 에너지 생산을 위해 풍력 터빈의 대형화와 해상풍력 단지 개발에 대한 많은 연구가 진행되고 있으며, 운영 및 관리(O&M, Operation and Management) 측면에서의 구조적 안전성 평가 및 유지관리의 중요성 또한 지속적으로 증가하고 있다. 안전성 평가를 위한 여러 항목 중 터빈 상부 즉, 허브 높이에서의 변위는 구조물의 강성에 의해 지배되는 직접적인 지표로, 구조물의 건전상태를 쉽게 평가할 수 있는 지표라 할 수 있다. 그러나 풍력 터빈과 같은 대형 구조물의 변위 측정은 장비의 한계로 직접적으로 측정하기에는 많은 어려움이 따른다. 따라서 이 연구에서는 (1) 경사와 가속도의 동적 계측응답 자료를 융합하여 간접적으로 변위를 추정하는 방법을 제안하고, (2) 제안된 방법을 제원이 공개되어 있는 NREL 5 MW급 풍력 터빈에 적용하여 경사계의 수, 경사계의 잡음 수준 및 계측 주파수에 따른 변위 추정의 정확도를 평가하였으며, 실제 변위 측정을 위한 매개변수 연구를 수행하였다.

• 대한인간공학회지
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• 제18권2호
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• pp.103-120
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• 1999

The purpose of this study was to evaluate the manual workload in repetitive wrist and finger motion. To evaluate manual workload, angular displacement of the joint, EMG of the muscle and subjective rating were studied. Both wrist motion and finger motion were studied. A screw-driving task was used for the wrist motion experiment. A keyboard typing task was used for the finger motion experiment. All finger joint angles and wrist angles were measured by an angle-measuring glove($CyberGlove^{TM}$, Virtual Technologies, Inc.). Surface EMG was recorded from FCU muscle and FDS muscle simultaneously with the angle measurement. Subjective ratings of exertion were also recorded using the modified Borg's CR-10 scale. Repetition rates of 0.5, 1, 2 motions per second were used with each task. As a result, manual workload increased with increasing repetitiveness. Peak spectral magnitude and frequency components corresponded closely with joint angular displacement amplitudes and repetition rates. Results of the correlation analysis showed that there were significant correlation among EMG, frequency-weighted motion and subjective measurement. Both EMG and frequency-weighted filtering showed consistent workload estimation with increasing task frequency. Subjective ratings showed slight over-estimation of the workload as the task frequency is increased.

• Journal of Advanced Marine Engineering and Technology
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• 제24권6호
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• pp.24-33
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• 2000

This paper investigates the relationship between J-integral and crack tip opening displacement, ${\delta}_t$ using Gordens results of numerical analysis. Estimation were carried out for several strength levels such as ultimate, flow, yield, ultimate-flow, flow-yield stress to determine the influence of strain hardening and the ratio of crack length to width on the $J-{\delta}_t$ relationship. It was found that for SE(B) specimens, the $J-{\delta}_t$ relationship can be applied to relate J to ${\delta}_t$ as follows $J=m_j{\times}{\sigma}_i{\times}{\delta}_t$ where $m_j=1.27773+0.8307({\alpha}/W)$, ${\sigma}_i:{\sigma}_U$, ${\sigma}_{U-F}={\frac{1}{2}} ({\sigma}_U+{\sigma}_F$), ${\sigma}_F$, ${\sigma}_F}$ $Y=({\sigma}_F+{\sigma}_Y)$, ${\sigma}_Y$

• 한국정밀공학회지
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• 제28권5호
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• pp.591-598
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• 2011

Tip position control of a flexible cantilever is difficult due to the non-minimum phase dynamics that result from the finite propagating speed of a mechanical wave along the cantilever. In this paper, we propose a method for the tip position control using a light and cheap accelerometer that does not bring any significant change to the dynamics of the cantilever system. The linear system identification model of the flexible cantilever is obtained with measurements by a laser displacement sensor. A Kalman estimator is designed with this model and calculates the estimated tip position with the acceleration data of the accelerometer that is attached on the tip of the cantilever. To verify reliability of the estimator, the estimated tip position is used to the feedback control system that uses a fuzzy logic controller. The control results are compared with those of the fuzzy control system where the real tip position is measured by a laser displacement sensor. Also, the performance of the estimator with the accelerometer is presented and discussed.

• Computers and Concrete
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• 제28권5호
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• pp.479-486
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• 2021

Reinforced concrete (RC) slabs are exposed to several static and dynamic effects during their period of service. Accordingly, there are many studies focused on the behavior of RC slabs under these effects in the literature. However, impact loading which can be more effective than other loads is not considered in the design phase of RC slabs. This study aims to investigate the dynamic behavior of two-way RC slabs under sudden impact loading. For this purpose, 3 different simply supported slab specimens are manufactured. These specimens are tested under impact loading by using the drop test setup and necessary measurement devices such as accelerometers, dynamic load cell, LVDT and data-logger. Mass and drop height of the hammer are taken constant during experimental study. It is seen that rigidity of the specimens effect experimental results. While acceleration values increase, displacement values decrease as the sizes of the specimens have bigger values. In the numerical part of the study, artificial neural networks (ANN) analysis is utilized. ANN analysis is used to model different physical dynamic processes depending upon the experimental variables. Maximum acceleration and displacement values are predicted by ANN analysis. Experimental and numerical values are compared and it is found out that proposed ANN model has yielded consistent results in the estimation of experimental values of the test specimens.

• 한국지반공학회논문집
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• 제26권3호
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• pp.47-57
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• 2010

Newmark 활동블록법을 이용하여 지진시 토사면의 영구변위를 산정할 경우 항복지진계수의 결정은 매우 중요하다. 그러나 기존에 제안된 항복지진계수는 일반적으로 한계평형해석에 기초하고 있어 역학적조건 중 평형조건만을 만족하므로 역학적으로 엄밀하지 못하다. 따라서 기 제안된 항복지진계수를 이용하여 토사면의 영구변위를 산정할 경우 문제점를 야기할 수 있다. 한계해석은 역학적으로 엄밀한 해의 범위를 산정하는 해석기법으로 항복지진계수의 역학적 엄밀성을 판단하는데 유용하다. 본 연구에서는 안정수, 사면경사, 지반물성 등에 따른 다양한 해석조건을 고려하여 실무적용에 유용한 항복지진계수 산정도표를 제안하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 C
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• pp.2090-2092
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• 2004

In this paper, the new measurement methodology of characteristics of the vibratory micro gyroscope using Quality factor and the resonant displacement was proposed. Because the Quality factor has a large error under the high Quality factor condition, it is difficult to analyze the characteristics of the vacuum packaged vibratory micro gyroscopes with the Quality factor. We analyzed mechanical characteristics of gyroscope with the value of Quality factor. We described measurement errors of mechanical characteristics of micro gyroscopes. The measured value of Quality factor is 47532 and error range of Quality factor is from -29.8 % to 73.9 %. The value of resonant displacement is 3.4${\mu}m$ and the measurement error is 2.9 %. From the result of Quality factor degradation and resonant displacement degradation, 1698 days and 1503 days were estimated as Time To Failure (TTF), respectively. The range of estimation error of Quality factor degradation and resonant displacement degradation is calculated from 1246 days to 1832 days and from 1456 days to 1537 days, respectively. We can analyze the characteristics of the vibratory gyroscope using the quality factor when the Quality factor is smaller than 10,000. Also we can analyze that using the resonant displacement when the Quality factor is larger than 10,000.

• 노동경제논집
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• 제27권3호
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• pp.75-111
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• 2004

현 연구에서는 NLSY 데이터를 이용하여 개개인이 노동시장에 진입한 이래로 겪은 모든 실직 및 실업 경험을 추출하고, 이를 바탕으로 특정 시점에서의 임금이 그 이전에 겪었던 실직의 횟수와 누적실업기간에 어떻게 영향을 받는가를 분석하였다. 기존의 연구들과는 달리 현 연구에서는 1회의 실업기간이 아닌 누적실업기간의 효과를 분석하였으며 나아가 실직 횟수와 누적 실업기간 중 어느 변수가 임금에 보다 큰 부정적 영향을 주는가를 분석하였다. 실직 횟수와 누적실업기간을 동시에 설명변수로 포함시킬 경우 남성 표본에서는 누적실업기간만이 유의하게 임금을 낮추는 것으로 나타나는 것과 대조적으로 여성 표본에서는 실직 횟수만이 임금에 부정적인 영향을 주는 것으로 나타났다. 남성의 경우 누적실업기간이 한 달 더 길어지면 임금은 0.4% 낮아지는 것으로 나타났으며 이 추정치는 표본 제약을 다양하게 바꾸고 추정모형을 달리하여도 변함 없이(robust) 나타났다. 한편 여성의 경우 추가적인 1회의 실직 경험이 가져다 주는 임금의 하락은 2%를 상회하는 것으로 나타났다.

• Geomechanics and Engineering
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• 제37권4호
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.