• Journal of Mechanical Science and Technology
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• 제14권9호
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• pp.913-919
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• 2000

The purpose of this study was to develop a new element removal method for ESO (Evolutionary Structural Optimization), which is one of the topology optimization methods. ESO starts with the maximum allowable design space and the optimal topology emerges by a process of removal of lowly stressed elements. The element removal ratio of ESO is fixed throughout topology optimization at 1 or 2%. BESO (bidirectional ESO) starts with either the least number of elements connecting the loads to the supports, or an initial design domain that fits within the maximum allowable domain, and the optimal topology evolves by adding or subtracting elements. But the convergence rate of BESO is also very slow. In this paper, a new element removal method for ESO was developed for improvement of the convergence rate. Then it was applied to the same problems as those in papers published previously. From the results, it was verified that the convergence rate was significantly improved compared with ESO as well as BESO.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.369-372
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• 2000

ESO(Evolutionary Structural Optimization) method is known that elements involved low stress value are removed from the previous model or that elements are added around elements involved high stress level on it and then the optimized model is obtained with required weight. Rejection ratio/addition ratio and evolutionary ratio are predefined and elements having lower/higher stress than reference stress, which average Mises stress on edge elements times rejection ratio, are deleted/added. In this study, when the plate having a cutout is subjected various in-plane load, a cutout shape is optimized using ESO method. ANSYS is used to analyse a finite element model and optimization procedure is made by APDL (ANSYS Parametric Design Language). ESO method is useful in rather than a complex structure optimization as well as a cutout shape optimization.

• 한국전산구조공학회논문집
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• 제24권3호
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• pp.319-328
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• 2011

이 논문은 ESO 기법을 기초로 한 Strut-Tie 모델의 구성을 제안하고 있다. 평면응력 요소를 사용한 기존의 ESO방법과 달리, ESO기법에 의해 최적화된 구조가 트러스와 비슷한 형태를 가지는 사실에 기인하여, Strut-Tie 모델을 통한 전단설계에 트러스 요소를 사용한 ESO기법을 새롭게 적용하였다. 예제들을 통해 제안된 방법이 가장 좋은 Strut-Tie 모델을 찾을 수 있음을 입증하였으며, 앞서 2차원 평면응력 요소와 Strut-Tie 모델의 연관성에 대한 연구를 통해 ESO방법이 효과적으로 사용될 수 있음은 물론 경험하지 못한 특히 복잡한 철근 콘크리트 구조물의 전단설계에 효과적으로 사용이 가능한 대안이 될 수 있을 것으로 판단된다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 가을 학술발표회 논문집
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• pp.295-302
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• 2002

This paper introduces a method to determine strut-tie models in reinforced concrete (RC) structures using the evolutionary structural optimization (ESO). Even though strut-tie models are broadly adapted in design of reinforced concrete members subjected to shear and torsion, conventional methods can hardly give correct models in RC members subjected to complex loadings and geometry conditions. In this paper, the basic idea of the ESO method is used to determine more rational strut-tie models. Since an optimum topology of structures, finally obtained by the ESO method, usually represents a truss-like structure, the ESO method can effectively be used in finding the best strut-tie model in RC structures. Several example structures are provided to demonstrate the capability of the proposed method in finding the best strut-tie model of each RC structure and to verify its efficiency in application to real design problems.

• 한국생산제조학회지
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• 제9권5호
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• pp.112-118
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• 2000

In case ESO(evolutionary structural optimization) which is one of topology optimization methods, the element removal ratio is fixed throughout topology optimization by 1 or 2%. As a result it has no flexibility for various types of structures and thus the rate of convergence might not be efficient. Thus various element removal methods were developed in order to improve the efficiency of ESO. In this paper, various element removal methods for ESO are compared with each other for a bracket and a short cantilever. In addition, a new improved bi-directional element removal method is suggested in order to obtain much better optimized topology. From the comparative results of the examples, it is verified that all of the developed various element removal methods are very effective, and the suggested element removal method is the most effective.

• 한국공간구조학회논문집
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• 제3권3호
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• pp.67-74
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• 2003

The purpose of this study is to improve convergence speed of topology optimization procedure using the existing ESO method and to deal with topology decision of the truss structures according to a boundary condition, such as cantilever type. At the existing ESO topology optimization procedure for the truss structures, the adjustment of member sizes according to target stress has been executed by increasing or reducing a very small value from each member size. In this case, it takes too much iteration till convergence. Accordingly, it is practically hard to obtain optimum topology for a large scale structures. For that reason, it is necessary to improve convergence speed of ESO method more effectively. During the topology decision procedure, member sizes are adjusted by calculating approximate solution for member sizes corresponding to the target stress at every step, the new member sizes are adjusted by such method are applied in FEA procedure of next step.

• Structural Engineering and Mechanics
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• 제7권4호
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• pp.401-412
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• 1999

The purpose of this paper is to show how the Evolutionary Structural Optimization (ESO) algorithm developed by Xie and Steven can be extended to optimal design problems of thin shells subjected to thermal loading. This extension simply incorporates an evolutionary iterative process of thermoelastic thin shell finite element analysis. During the evolution process, lowly stressed material is gradually eliminated from the structure. This paper presents a number of examples to demonstrate the capabilities of the ESO algorithm for solving topology optimization and thickness distribution problems of thermoelastic thin shells.

• 한국농공학회지
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• 제44권2호
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• pp.127-135
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• 2002

Due to the fact that the design of a reinforced concrete structure changes in accordance with its shape and assigned load, total automation of the design system has not been achieved. For instance, since there is no general rule about setting up reinforcing steel quantity and arrangement location, it is simply not feasible to automatically decide the reinforcing arrangement location. In this study, the ESO(evolutionary structural optimization) technique and its related issues will be discussed. The ESO techniques is determined the reasonable load path which is traveling of load between in-flow and out-flow at a concrete structure using numerical analysis. And the results applied to the steel arrangement in reinforced concrete structures. The optimal algorithm, which determines the terminal criteria during ESO process, has been updated by using the obtained results. And the load path within the member has been determined automatically.

• 한국농공학회논문집
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• 제46권4호
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• pp.65-71
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• 2004

Stress Path Search Model of Evolutionary Structural Successive Optimization (SPSMESO) using Triangular Irregular Network(TIN) was developed for improving over burden at initial design of ESO and strict stress direction of strut-and-tie model and truss model. TIN was applied for discretizing structures in flexible stress path and segments of TIN was analyzed as one-dimensional line element for calculating stress. Finally, stress path was searched using ESO algorithm. SPSMESO was efficient to express the direction of stress for 2D structure and time saving.

• 대한기계학회논문집A
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• 제25권9호
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• pp.1444-1451
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• 2001

The evolutionary structural optimization(ESO) method has been under continuous development since 1992. The bidirectional evolutionary structural optimization(BESO) method is made of additive and removal procedure. The BESO method is very useful to search the global optimum and to reduce the computational time. This paper presents the ranked bidirectional evolutionary structural optimization(R-BESO) method which adds elements based on a rank, and the performance indicator which can estimate a fully stressed model. The R-BESO method can obtain the optimum design using less iteration number than iteration number of the BESO.