• Earthquakes and Structures
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• 제20권2호
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• pp.225-238
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• 2021

Supplemental passive dampers are widely employed to improve the structural performance of buildings under seismic excitations. Nevertheless, the added damping could be counter-productive if the axial forces induced by the damper reaction forces are not routed properly in the columns. A few researchers engaged to optimize the width-wise damper arrangement to improve the delivered path of the axial column forces. However, most of these studies are limited under the design-based seismic level and few of them has evaluated the collapse performance of buildings under strong earthquakes. In this paper, the strategic width-wise placement method of viscous dampers is explored regarding the building performance under collapse state. Two realistic steel buildings with different storeys are modelled and compared to explore higher mode effects. Each building is designed with four different damper arrangement scenarios based on a classic damper distribution method. Both a far-fault and a near-fault seismic environment are considered for the buildings. Incremental Dynamic Analysis (IDA) is performed to evaluate the probability of collapse and the plastic mechanism of the retrofitted steel buildings.

• 대한기계학회논문집A
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• 제35권10호
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• pp.1223-1228
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• 2011

가동코일형 리니어 액추에이터는 다른 형식의 액추에이터에 비해 구조가 간단하고 제어가용이하여 다양한 산업 분야에 활용되고 있다. 본 연구에서는 리니어 액추에이터의 가동 특성을 향상시키기 위해 가동자의 모든 동작점에서의 추력을 반영한 목적 함수를 구성하고 최적설계 문제를 정식화하였다. 명확한 형상표현을 위해 레벨셋 함수를 설계변수로 설정하여 최적설계를 진행하고 성능과 생산성을 동시에 만족하는 액추에이터를 설계하기 위해 페이즈 필드 모델의 개념을 최적설계에 적용하여 최종형상의 단순화를 고려하였다. 제안한 기법의 효용성을 확인하기 위해 액추에이터 진동과 소음의 원인인 추력의 변동폭을 최소화하기 위한 코어 설계를 수행하여 추력의 변동을 감소시킬 수 있는 최적 형상을 제시하였고 복잡도 계수에 의한 최종 형상의 단순화도 확인하였다.

• 한국산학기술학회논문지
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• 제16권4호
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• pp.2356-2363
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• 2015

본 연구는 볼트강도등급 10.9에 의해 추천되는 조임 토크 640~800(Nm)가 볼트-너트 체결체에 가해졌을 때 풀림 방지를 위한 코일 스프링의 삽입 및 시뮬레이션 기반 설계 최적화에 관한 것이다. 먼저 볼트-너트-코일스프링으로 구성된 조립체에 대하여 등가응력에 기반을 둔 구조 안전성 판단을 위한 조립체 구조해석 시뮬레이션을 수행한다. 그리고 이러한 해석 시뮬레이션 결과로부터 설계 개선안 도출을 위한 설계전략을 수립한다. 또한 이 전략 안에서 기존 설계의 성능을 개선해 나가는 반복 과정을 제안한다. 이 과정에서는 먼저 반응표면법을 사용하여 설계 파라미터 후보점을 찾고, 그 후보점의 반응값과 실제 시뮬레이션 결과를 비교함으로써 설계 후보점(코일스프링 감감수 N = 6)이 최적인지를 검증한다.

• Computers and Concrete
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• 제9권5호
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• pp.327-340
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• 2012

The search for a design that meets both performance and safety, with minimal cost and lesser environmental impact was always the goal of structural engineers. In general, the design of conventional reinforced concrete structures is an iterative process based on rules of thumb established from the personal experience and intuition of the designer. However, such procedure makes the design process exhaustive and only occasionally leads to the best solution. In such context, this work presents the development and implementation of a mathematical formulation for obtaining optimal sections of reinforced concrete columns subjected to uniaxial flexural compression, based on the verification of strength proposed by the Brazilian standard NBR 6118 (ABNT 2007). To minimize the cost of the reinforced concrete columns, the Simulated Annealing optimization method was used, in which the amount and diameters of the reinforcement bars and the dimensions of the columns cross sections were considered as discrete variables. The results obtained were compared to those obtained from the conventional design procedure and other optimization methods, in an attempt to verify the influence of resistance class, variations in the magnitudes of bending moment and axial force, and material costs on the optimal design of reinforced concrete columns subjected to uniaxial flexural compression.

• Structural Engineering and Mechanics
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• 제49권1호
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• pp.65-80
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• 2014

This paper reports on the development of a minimum cost design model and its application for obtaining economic designs for reinforced High Strength Concrete (HSC) T-sections in bending under ultimate limit state conditions. Cost objective functions, behavior constraint including material nonlinearities of steel and HSC, conditions on strain compatibility in steel and concrete and geometric design variable constraints are derived and implemented within the Conjugate Gradient optimization algorithm. Particular attention is paid to problem formulation, solution behavior and economic considerations. A typical example problem is considered to illustrate the applicability of the minimum cost design model and solution methodology. Results are confronted to design solutions derived from conventional design office methods to evaluate the performance of the cost model and its sensitivity to a wide range of unit cost ratios of construction materials and various classes of HSC described in Eurocode2. It is shown, among others that optimal solutions achieved using the present approach can lead to substantial savings in the amount of construction materials to be used. In addition, the proposed approach is practically simple, reliable and computationally effective compared to standard design procedures used in current engineering practice.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.12-14
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• 2005

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) by means of genetically optimized fuzzy polynomial neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms(GAs). The conventional SOFPNNs hinges on an extended Group Method of Data Handling(GMDH) and exploits a fixed fuzzy inference type in each FPN of the SOFPNN as well as considers a fixed number of input nodes located in each layer. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, a collection of the specific subset of input variables, and the number of membership function) and addresses specific aspects of parametric optimization. Therefore, the proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series).

• 한국기계가공학회지
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• 제11권3호
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• pp.55-61
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• 2012

The structure of winch brake disk was successfully designed and developed based on sizing optimization. In this research, static analysis was performed by commercial software ANSYS v12.0. To simulate the working process of disk brake, the real properties of materials and working conditions were considered. Based on the results of the static structural analysis, the existing designs of the brake discs were optimized. Among existing designs, there are three cases that have achieved an efficient light weight around 200g. As a result, the optimized weight of each case was 3.41kg, 3.42kg, and 3.44kg, respectively. Finally, through prototyping and performance testing, the stability of the optimized brake disc was verified. Although, this free-fall winch brake disk had been developed in design and evaluation techniques, more detailed plans for developing the disk brake structure were also proposed as a further study based on this research.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.207-209
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• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• 대한기계학회논문집A
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• 제29권10호
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• pp.1369-1376
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• 2005

In structural design, the design variables are frequently selected from certain discrete values. Various optimization algorithms have been developed fDr discrete design. It is well known that many function evaluations are needed in such optimization. Recently, sequential algorithm with orthogonal arrays (SOA), which is a search algorithm for a local minimum in a discrete space, has been developed. It considerably reduces the number of function evaluations. However, it only finds a local minimum and the final solution depends on the initial values of the design variables. A new algorithm is proposed to adopt a genetic algorithm (GA) in SOA. The GA can find a solution in a global sense. The solution from the GA is used as the initial design of SOA. A sequential usage of the GA and SOA is carried out in an iterative manner until the convergence criteria are satisfied. The performance of the algorithm is evaluated by various examples.

• 제어로봇시스템학회논문지
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• 제11권2호
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.