• Structural Engineering and Mechanics
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• 제81권6호
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• pp.677-689
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• 2022

Generally, the goal of seismic retrofit design of an existing structure using energy dissipation devices is to determine the optimum design parameters of a retrofit device to satisfy a specified limit state with minimum cost. However, the presence of multiple parameters to be optimized and the computational complexity of performing non-linear analysis make it difficult to find the optimal design parameters in the realistic 3D structure. In this study, genetic algorithm-based optimal seismic retrofit methods for determining the required number, yield strength, and location of steel slit dampers are proposed to retrofit an asymmetric soft first-story structure. These methods use a multi-objective and single-objective evolutionary algorithms, each of which varies in computational complexity and incorporates nonlinear time-history analysis to determine seismic performance. Pareto-optimal solutions of the multi-objective optimization are found using a non-dominated sorting genetic algorithm (NSGA-II). It is demonstrated that the developed multi-objective optimization methods can determine the optimum number, yield strength, and location of dampers that satisfy the given limit state of a three-dimensional asymmetric soft first-story structure. It is also shown that the single-objective distribution method based on minimizing plan-wise stiffness eccentricity turns out to produce similar number of dampers in optimum locations without time consuming nonlinear dynamic analysis.

• Steel and Composite Structures
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• 제25권4호
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• pp.473-484
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• 2017

In this study a seismic retrofit scheme for a building structure was presented using steel plate slit dampers. The energy dissipation capacity of the slit damper used in the retrofit was verified by cyclic loading test. Genetic algorithm was applied to find out the optimum locations of the slit dampers satisfying the target displacement. The seismic retrofit of the model structure using the slit dampers was compared with the retrofit with enlarging shear walls. A simple damper distribution method was proposed using the capacity spectrum method along with the damper distribution pattern proportional to the inter-story drifts. The validity of the simple story-wise damper distribution procedure was verified by comparing the results of genetic algorithm. It was observed that the capacity-spectrum method combined with the simple damper distribution pattern leaded to satisfactory story-wise distribution of dampers compatible with the optimum solution obtained from genetic algorithm.

• 한국지진공학회논문집
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• 제11권3호
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• pp.43-52
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• 2007

본 논문에서는 기존 도로교의 합리적이고 일관적인 내진성능향상 방법 선정을 위한 가중치 평가기법을 제안하였다. 한반도의 증가하는 지진위험도를 반영하여, 현재 기존교량의 내진성능향상 작업이 다양한 공법을 이용하여 진행 중이나 최적의 공법을 선정하기 위한 적절한 판단기준이 부재한 형편이다. 이에 제안한 기존가중치 평가기법은 구조적 적합성, 경제성, 환경적 영향, 시공성, 유지관리 측면의 다섯 가지 영향인자의 가중치를 부여하여 최적의 내진 보강방안을 선정하는 방법이다. 제안된 가중치 평가기법을 공용중인 도로교 160개교에 적용한 결과 최고점수는 실제 최종 보강방안과 대부분 일치하여 적절한 가중치로 설정되었다고 판단된다. 제안하는 방법을 기존교량의 내진 보강방안 선정 시에 활용한다면, 사회적 비용을 최소화하는 보다 합리적이고 일관적인 보강이 가능할 것이다.

• Earthquakes and Structures
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• 제25권6호
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• 한국구조물진단유지관리공학회 논문집
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• 제17권5호
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• pp.136-145
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• 2013

최근 발생한 대규모 지진으로 구조물의 내진보강에 대한 사회적 관심도가 높아지고 있다. 특히 내진 설계가 반영되지 않은 기둥은 취성적인 파괴로 구조물 전체붕괴를 유발하기 때문에 내진보강이 적용 되어야한다. 과거에는 단면증설법, 강판보강법등이 주로 적용되었고 최근에는 복합재료의 장점을 이용한 섬유보강법이 선호되고 있다. 그러나 이러한 보강법들은 구조물의 물리적 손상을 유발하며, 작업공간과 시간소비가 크다는 단점이 있다. 본 연구에서는 기존 보강법의 단점을 보강하여 복합재료 (Fiber reinforced polymer)와 Aluminum 체결부 이용한 FRP 내진보강재를 개발하였다. 비선형 유한요소 해석프로그램을 통해 개발된 FRP 내진보강재의 최적 보강량을 결정하였다.