• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.2 s.54
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• pp.137-144
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• 2009

This paper provides the results from evaluating the stiffness of Hi-Form joint by an experiment and the system identification method using the dynamic modal data, and the reasonable modeling method of Hi-Form joint which is proposed for improved stair construction recently. Based on the crack pattern and load-displacement relationship and the damage distribution, it can be judged that Hi-Form joint can't fully transfer the forces between the elements linked, and we propose that the joint is modeled as a element which have a stiffness with 50% decrease.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.9-20
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• 1999

A method is proposed to estimate the joint damages of a steel structure from modal data using the neural networks technique. The beam-to-column connection in a steel frame structure is represented by a zero-length rotational spring of the end of the beam element, and the connection fixity factor is defined based on the rotational stiffness so that the factor may be in the range 0~1.0. Then, the severity of joint damage is defined as the reduction ratio of the connection fixity factor. Several advanced techniques are employed to develop the robust damage identification technique using neural networks. The concept of the substructural indentification is used for the localized damage assessment in the large structure. The noise-injection learning algorithm is used to reduce the effects of the noise in the modal data. The data perturbation scheme is also employed to assess the confidence in the estimated damages based on a few sets of actual measurement data. The feasibility of the proposed method is examined through a numerical simulation study on a 2-bay 10-story structure and an experimental study on a 2-story structure. It has been found that the joint damages can be reasonably estimated even for the case where the measured modal vectors are limited to a localized substructure and the data are severely corrupted with noise.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1999.04a
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• pp.300-307
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• 1999

신경망기법을 이용한 구조물접합부의 손상평가기법을 제안하였다. 신경망기법의 성능을 개선하기 위하여 노이즈첨가학습을 수행하였으며 효과적인 손상평가를 위하여 부분구조추정법 및 data perturbation scheme을 도입하였다. 10층 프레임구조물에 대한 수치해석과 2층 프레임구조물에 대한 실험연구를 통하여 제안기법을 검증하였다 계측지점이 부분구조로 제한되고 계측자료가 노이즈를 포함하는 경우에는 제안기법이 효과적으로 적용될수 있음을 알 수 있었으며 실험을 통하여 실제 구조물에 대한 제안기법의 적용성을 평가힐 수 있었다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.315-318
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• 2011

본 논문에서는 구조물 연결부의 실시간 손상 검색을 통해 이상이 감지되었을 경우 자가치유까지 가능한 지능형 볼트접합부 시스템에 관한 실험적 연구결과가 제시되었다. 지능형 센서인 PZT센서의 전기-역학적 커플링 특성을 이용한 전기역학적 임피던스 기반의 구조물 건전성 평가 방법이 사용되었다. 전기역학적 임피던스의 측정을 통한 계측값을 베이스라인 값과 비교하는 손상 평가를 통해 구조물 볼트접합부의 볼트풀림 손상을 진단하고, 손상은 손상지수 RMSD를 통하여 정량화되었다. 볼트접합부의 손상이 감지되었을 경우 형상기억합금(SMA) 와셔에 부착되어있는 히팅 필름에 전원을 가함으로써 형상기억합금에 열을 가하고, 가열된 형상기억합금 와셔는 축방향으로 팽창을 함으로써 잃었던 볼트의 토크력을 회복시켜주었다. 실험 결과, 제안된 전기역학적 임피던스 기반의 구조물 건전성 평가기법과 형상기억합금 와셔 기반의 볼트접합부 자가치유 시스템의 성능 평가와 검증이 이루어졌다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.692-695
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• 2010

일반적으로 모델개선에서는 부재단위의 강성을 파악하기 때문에 구조물의 취약부인 부재단부의 손상이 집중될 경우 손상의 형태를 세밀히 파악하기 어려우며 손상된 구조물의 거동을 정확하게 모사하기 어려운 단점이 있었다. 이를 해결하기 위해서는 부재 단부에 발생한 손상을 고려할 수 있는 좀 더 정밀한 해석 모델을 통한 모델개선이 필요하다. 본 연구에서는 부재 단부에 반강접 접합을 가지는 해석모델을 사용해 모델 개선을 실시하고 이를 통해 접합부의 손상 평가와 손상 구조물의 거동을 파악하였다. 제안된 방법을 5층 1경간의 RC 벽식 실험체의 손상탐지에 적용하였으며 그 결과 부재단위 모델을 사용할 때보다 더욱 정확하게 구조물의 손상을 평가하고 거동을 모사할 수 있었다.

• Journal of Korean Society of Steel Construction
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• v.23 no.5
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• pp.629-636
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• 2011

This paper describes the smart structural system that uses smart materials for real-time monitoring and active control of bolted joints in steel structures. The impedance-based structural health monitoring (SHM) techniques, which utilize the electro-mechanical coupling property of piezoelectric materials, was used to detect loose bolts in bolted joints. By monitoring the measured electrical impedance and comparing it with the measured baseline, a bolt loosening damage was detected. The damage was evaluated quantitatively using the damage metrics in conductance signature with respect to the healthy states. When loosening damage was detected in the bolted joint, the external heater actuated the shape memory alloy (SMA) washer. Then the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. An experiment was conducted by integrating the piezoelectric-material-based SHM function and the SMA-based active control function on a bolted joint, after which the performance of thesmart self-healing joint system was investigated.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.1
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• pp.107-114
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• 2018

In this study, the damage detection method is proposed for the rotational stiffness of connections in steel moment frames by using artificial neural network(ANN). The flexural moment of columns, natural frequencies, modeshapes are used for the input layer in ANN while the damage index, that signify the damage level, is used for the output layer in ANN. The 5-story steel moment frame as an example structure is used to generate the train and test data. Total number of damage scenarios considered is 829. From the results of application, it is shown that the proposed method can accurately estimate the location and level of damages.

• Proceedings of the KWS Conference
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• v.43
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• pp.144-146
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• 2004

• Proceedings of the KWS Conference
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• 2001.05a
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• pp.127-129
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• 2001

• Journal of the Microelectronics and Packaging Society
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• v.29 no.2
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• pp.113-119
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• 2022

In order to improve the mechanical reliability of next-generation electronic devices including flexible, wearable devices, a high level of mechanical reliability is required at various flexible joints. Organic adhesive materials such as epoxy for bonding existing polymer substrates inevitably have an increase in the thickness of the joint and involve problems of thermodynamic damage due to repeated deformation and high temperature hardening. Therefore, it is required to develop a low-temperature bonding process to minimize the thickness of the joint and prevent thermal damage for flexible bonding. This study developed flexible laser transmission welding (f-LTW) that allows bonding of flexible substrates with flexibility, robustness, and low thermal damage. Carbon nanotube (CNT) is thin-film coated on a flexible substrate to reduce the thickness of the joint, and a local melt bonding process on the surface of a polymer substrate by heating a CNT dispersion beam laser has been developed. The laser process conditions were constructed to minimize the thermal damage of the substrate and the mechanism of forming a CNT junction with the polymer substrate. In addition, lap shear adhesion test, peel test, and repeated bending experiment were conducted to evaluate the strength and flexibility of the flexible bonding joint.