PURPOSES : Previously, the expansion state of the concrete pavement in which AAR occurred could not be determined. Because the current situation has not been evaluated, it has been difficult to prepare an appropriate response. In this study, a method for calculating the expansion amount of concrete pavement using the stiffness damage test (SDT) is proposed. METHODS : The SDT method was examined through a literature review. For the laboratory tests, specimens that generated AAR were produced based on the mix design (2018) of the Korea Expressway Corporation. SDT was used to calculate various mechanical properties, and their correlation with the expansion amount was reviewed. RESULTS : Using the SDT, various mechanical properties(elastic modulus, hysteresis area, plastic deformation, plastic deformation index, stiffness damage index, and nonlinear index) were calculated based on the expansion rate of the AAR. The elastic modulus was evaluated as the best predictor of the expansion rate. Thus, if the elastic modulus is calculated using SDT, a prediction equation can be used to calculate the amount of AAR expansion. This equation will need to be supplemented by further research. CONCLUSIONS : SDT was used to confirm that the expansion state due to the AAR of the concrete pavement could be indirectly evaluated. Among the mechanical properties related to SDT, the elastic modulus was found to be the most suitable for predicting the amount of expansion.

The structural performance of a vehicle can be evaluated by the static and dynamic structural analyses which predict the amounts of deformation & stiffness, and the static analysis should be done first. Another important aspect to be considered in the design process is crashworthiness, because a structurally sturdy vehicle body may be overdesigned with the excessive strength and durability standards. The ideal condition of a body structure is to absorb the impact load at a certain level of local deformation, to distribute the load to each structure adequately, and to prevent the excessive stress concentration and deformation. This paper is the result of the consideration of vibration characteristic for structure stiffness estimation of automotive body through the finite element modeling.

The structural performance of a vehicle can be evaluated by the static and dynamic structural analyses which predict the amount of deformation, stiffness. And the static analysis should be done first. Another important aspect to be considered in the design process is crashworthiness, because a structurally sturdy vehicle body may be overdesigned with excessive strength and durability standards. The ideal condition of a body structure is to absorb impact load at a certain level of local deformation, to distribute the load to each structure adequately, and to prevent excessive stress concentration and deformation. This paper is the result of the consideration of automotive body, bending and torsional stiffness for structure stiffness estimation of automotive body through finite element modeling.

As for the recent need for maintenance of aging structures and tall buildings, the traditional structural inspection and management methods are expected to be enhanced with the automated structural health monitoring system. The system identification technique is deem to be a core deem of the structural health monitoring. In this study, a methodology of structural stiffness estimation is proposed to identify the state space model of the target structure from the dynamic behavior measurement data of the structure. Experimental verification of the physical quantity estimation technique is conducted.

본 연구는 인공신경망을 이용해 철골모멘트골조의 접합부 손상을 예측하는 기법을 제안한다. 인공신경망의 입력층에는 기둥 부재 의 휨모멘트, 고유진동수, 모드형상 정보가 사용되며, 출력층에는 구조물 접합부의 회전강성 손상지표가 사용한다. 손상지표는 각 접합부의 손 상정도를 의미한다. 5층 철골모멘트골조 예제의 수치해석을 통해 훈련 및 검증용 데이터를 생성한다. 총 829가지의 손상 시나리오가 고려된다. 시뮬레이션은 OpenSees를 이용해 반복 실행하여 데이터를 얻도록 하였으며, 훈련용 데이터를 생성할 때 회전 강성의 손상은 1.0, 0.75, 0.5 등 세 가지 중 하나의 값을 가지도록 하였다. 예제 검증을 통해 제시하는 기법은 손상 위치 및 수준을 정확하게 예측하는 것으로 나타났다. 제시하는 기법은 손상지표, 1차, 2차 고유진동수 및 모드형상 등에 대해 매우 유사한 결과를 제시하는 것으로 확인되었다.

In this study, the artificial neural network (ANN) based estimation method for rotational stiffness of column-beam connection in steel moment frames is presented. Three story and one bay steel moment frame is used as the example structure. Natural periods and mode-shapes are used as the input data and the rotational stiffness values of connections are used as the output data.

To establish how we can estimate the stability of traditional wood structures by using commercial structural analysis programs, it is important to calculate the stiffness of the Gong-Po. So, the estimating method of its axial stiffness is proposed on the deformation of each member which forms the Gong-Po.

본 연구에서는 기존의 모르타르 충전식 슬리브 철근이음에 대한 부착강도식으로부터 유도한 이 철근이음의 파괴모드 추정방법을 이용하여, AIJ 규준에 의하여 평가한 이 슬리브 철근이음의 강성을 검토하였다. 이것을 위하여 261개 모르타르 충전식 슬리브 철근이음의 기존 실험자료를 채택하여 실험의 결과를 분석한 결과에 의하면 모르타르 충전식 철근이음의 파괴모드 추정방법은 돌기가 없는 강관 슬리브에 SD500 철근을 사용한 철근이음을 제외한 모르타르 충전식 슬리브 철근이음에 대한 강성을 효과적으로 평가할 수 있었다. 그리고 이 슬리브 철근이음의 파괴모드 추정방법에 적용하여 철근의 인장파단 영역에 있는 실험체 중에 주물 슬리브와 돌기가 있는 강관 슬리브를 사용한 실험체에서 SD400 철근을 사용한 경우는 98%, SD500 철근을 사용한 경우는 모든 실험체가 단조가력 시의 강성이 AIJ 규준의 A급 이상인 것으로 나타났고, 철근의 인장파단 영역에 있는 모든 실험체는 슬리브의 종류와 슬리브에 매입한 철근 종류에 관계없이 반복가력 시의 강성이 AIJ 규준의 A급 이상인 것으로 나타났다.