• Title, Summary, Keyword: Structural optimization

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Hopfield neuron based nonlinear constrained programming to fuzzy structural engineering optimization

  • Shih, C.J.;Chang, C.C.
    • Structural Engineering and Mechanics
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    • v.7 no.5
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    • pp.485-502
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    • 1999
  • Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.

Structural Cost Optimization for Building Frame System Using High-Strength Steel Members (고강도 강재를 사용한 건물골조방식 구조물의 구조비용 최적화)

  • Choi Sang-Hyun;Kwon Bong-Keun;Kim Sang-Bum;Seo Ji-Hyun;Kwon Yun-Han;Park Hyo-Seon
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • pp.541-548
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    • 2006
  • This study presents a structural cost optimization method for building frame system using high-strength steel members. In, this optimization method, the material cost of steel member is involved in objective function to find the optimal cost of building frame systems. Genetic Algorithm is adopted to optimizer to find structural cost optimization. The proposed adapted to structural design of 3.5 stories example buildings with buildings frame systems. As a result, The proposed optimization method can be effectively adapted to cost optimization of building frame systems using high-strength steel members.

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Topological optimized design considering dynamic problem with non-stochastic structural uncertainty

  • Lee, Dong-Kyu;Starossek, Uwe;Shin, Soo-Mi
    • Structural Engineering and Mechanics
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    • v.36 no.1
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    • pp.79-94
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    • 2010
  • This study shows how uncertainties of data like material properties quantitatively have an influence on structural topology optimization results for dynamic problems, here such as both optimal topology and shape. In general, the data uncertainties may result in uncertainties of structural behaviors like deflection or stress in structural analyses. Therefore optimization solutions naturally depend on the uncertainties in structural behaviors, since structural behaviors estimated by the structural analysis method like FEM need to execute optimization procedures. In order to quantitatively estimate the effect of data uncertainties on topology optimization solutions of dynamic problems, a so-called interval analysis is utilized in this study, and it is a well-known non-stochastic approach for uncertainty estimate. Topology optimization is realized by using a typical SIMP method, and for dynamic problems the optimization seeks to maximize the first-order eigenfrequency subject to a given material limit like a volume. Numerical applications topologically optimizing dynamic wall structures with varied supports are studied to verify the non-stochastic interval analysis is also suitable to estimate topology optimization results with dynamic problems.

Topology Optimization

  • 박연규
    • CDE review
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    • v.3 no.2
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    • pp.89-92
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    • 1997
  • 이 글에서 소개하는 topology optimization은 structural optimization의 한 분야로서 최근 10여년 동안 급격하게 발전되어 온 분야이다. Structural optimization은 오랜 역사(일반적으로 최초의 structural optimization은 17세기 Galileo에 의하여 되어졌다고 받아들임)를 가지고 발달되어 왔음에도 불구하고 아직도 최적화 방법론과 응용 관점에서 빠르게 발전되고 있다. 이 분야는 사회적인 요구(한정된 자원과 에너지, 안전도, 환경문제)와 컴퓨터 관련 학문(고성능 컴퓨터, computational geometry, finite element method)의 발달에 힘입어 최근 30년간 많은 진전이 있었다.

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STEP-Based Information Exchange for Structural Analysis and Optimization (STEP을 이용한 구조해석 및 최적설계 정보교환)

  • Baek, Ju-Hwan;Min, Seung-Jae
    • Korean Journal of Computational Design and Engineering
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    • v.12 no.1
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    • pp.8-14
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    • 2007
  • In the product design process computer-aided engineering and optimization tolls are widely utilized in order to reduce the total development time and cost. Since several simulation tools are involved in the process, information losses, omissions, or errors are common and the importance of seamless information exchange among the tools has been increased. In this work, ISO STEP standards are adopted to represent the neutral format for structural analysis and optimization. The schema of AP209 defined the information of finite element analysis is used and the new schema is proposed to describe the information of structural optimization based on the STEP methodology. The schema is implemented by EXPRESS, information modeling language, and ST-Developer is employed to generate C++ classes and STEP Rose Library by using the schema denoted. To substantiate the proposed approach, the information access interfaces of the finite element modeling software (FEMAP), structural optimization software(GENESIS) and in-house topology optimization program are developed. Examples are shown to validate the information exchange of finite element analysis and structural optimization using STEP standards.

Optimal design of reinforced concrete plane frames using artificial neural networks

  • Kao, Chin-Sheng;Yeh, I-Cheng
    • Computers and Concrete
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    • v.14 no.4
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    • pp.445-462
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    • 2014
  • To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. There have been many packages that can be employed to analyze reinforced concrete plane frames. However, because most structural analysis packages suffer from closeness of systems, it is very difficult to integrate them with optimization packages. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrates Design, Analysis, Modeling, Definition, and Optimization phases into an integration environment as follows. (1) Design: first generate many possible structural design alternatives. Each design alternative consists of many design variables X. (2) Analysis: employ the structural analysis software to analyze all structural design alternatives to obtain their internal forces and displacements. They are the response variables Y. (3) Modeling: employ artificial neural networks to build the models Y=f(X) to obtain the relationship functions between the design variables X and the response variables Y. (4) Definition: employ the design variables X and the response variables Y to define the objective function and constraint functions. (5) Optimization: employ the optimization software to solve the optimization problem consisting of the objective function and the constraint functions to produce the optimum design variables. The RC frame optimization problem was examined to evaluate the DAMDO approach, and the empirical results showed that it can be solved by the approach.

Dynamic sensitivity analysis and optimum design of aerospace structures

  • Gu, Yuanxian;Kang, Zhan;Guan, Zhenqun;Jia, Zhiwen
    • Structural Engineering and Mechanics
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    • v.6 no.1
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    • pp.31-40
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    • 1998
  • The research and applications of numerical methods of design optimization on structural dynamic behaviors are presented in this paper. The emphasis is focused on the dynamic design optimization of aerospace structures, particularly those composed of composite laminate and sandwich plates. The methods of design modeling, sensitivity analysis on structural dynamic responses, and the optimization solution approaches are presented. The numerical examples of sensitivity analysis and dynamic structural design optimization are given to demonstrate the effectiveness of the numerical methods.

Use of design optimization techniques in solving typical structural engineering related design optimization problems

  • Fedorik, Filip;Kala, Jiri;Haapala, Antti;Malaska, Mikko
    • Structural Engineering and Mechanics
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    • v.55 no.6
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    • pp.1121-1137
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    • 2015
  • High powered computers and engineering computer systems allow designers to routinely simulate complex physical phenomena. The presented work deals with the analysis of two finite element method optimization techniques (First Order Method-FOM and Subproblem Approximation Method-SAM) implemented in the individual Design Optimization module in the Ansys software to analyze the behavior of real problems. A design optimization is a difficult mathematical process, intended to find the minimum or maximum of an objective function, which is mostly based on iterative procedure. Using optimization techniques in engineering designs requires detailed knowledge of the analyzed problem but also an ability to select the appropriate optimization method. The methods embedded in advanced computer software are based on different optimization techniques and their efficiency is significantly influenced by the specific character of a problem. The efficiency, robustness and accuracy of the methods are studied through strictly convex two-dimensional optimization problem, which is represented by volume minimization of two bars' plane frame structure subjected to maximal vertical displacement limit. Advantages and disadvantages of the methods are described and some practical tips provided which could be beneficial in any efficient engineering design by using an optimization method.

A new hybrid meta-heuristic for structural design: ranked particles optimization

  • Kaveh, A.;Nasrollahi, A.
    • Structural Engineering and Mechanics
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    • v.52 no.2
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    • pp.405-426
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    • 2014
  • In this paper, a new meta-heuristic algorithm named Ranked Particles Optimization (RPO), is presented. This algorithm is not inspired from natural or physical phenomena. However, it is based on numerous researches in the field of meta-heuristic optimization algorithms. In this algorithm, like other meta-heuristic algorithms, optimization process starts with by producing a population of random solutions, Particles, located in the feasible search space. In the next step, cost functions corresponding to all random particles are evaluated and some of those having minimum cost functions are stored. These particles are ranked and their weighted average is calculated and named Ranked Center. New solutions are produced by moving each particle along its previous motion, the ranked center, and the best particle found thus far. The robustness of this algorithm is verified by solving some mathematical and structural optimization problems. Simplicity of implementation and reaching to desired solution are two main characteristics of this algorithm.

Micro Genetic Algorithms in Structural Optimization and Their Applications (마이크로 유전알고리즘을 이용한 구조최적설계 및 응용에 관한 연구)

  • 김종헌;이종수;이형주;구본홍
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • pp.225-232
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    • 2002
  • Simple genetic algorithm(SGA) has been used to optimize a lot of structural optimization problems because it can optimize non-linear problems and obtain the global solution. But, because of large evolving populations during many generations, it takes a long time to calculate fitness. Therefore this paper applied micro-genetic algorithm(μ -GA) to structural optimization and compared results of μ -GA with results of SGA. Additionally, the Paper applied μ -GA to gate optimization problem for injection molds by using simulation program CAPA.

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