• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.335-347
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• 2003

본 논문에서는 설계 하중에 지배되는 구조물에 있어서, 입력 파라미터들의 불확실성을 표준편차와 패턴의 변동, 두 차원에서 접근, 처리할 수 있는 방안을 제시하기 위해서 구조물에 입력으로 작용하는 하중 패턴의 결정과 구조물의 형상의 진화를 동시에 고려할 수 있는 Co-Evolutionary Structural Design framework라 명명한 새로운 구조 설계 방식을 개발하였다. 공학자의 직관과 경험 의존적인 하중을 대상으로 최적화된 구조물은, 성능에 완벽한 안전을 보장해 줄 수 없으며, 이에 관한 문제를 해결하기 위해서 주어진 상황 속에서 다양한 하중이 작용하더라도 안전할 수 있는 구조물의 설계 방식에 관해서 설명한다. 본 프레임워크는 연성을 가지는 두 Disciplinary Modules, 즉 구조 형상설계와 하중설계로 이루어지며 하중에 관한 DB로 연결되어 순차적인 MDO 설계과정을 거치게 된다. 두 Discipline은 설계과정을 거치면서 상호 견제의 틀 속에서 진화하며 기존 방식과 달리 극한 하중 패턴을 스스로 찾아서 설계 반영하는 특징을 가진다. 본 접근 방식의 유용성을 평가하기 위해서 10-bar truss 구조물과 Jacket-Type 구조물로 테스트해 보았다.

• Proceeding of KASS Symposium
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• 2005.05a
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• pp.246-253
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• 2005

The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algorithm are searching methods for optimum values. The object of this reserch is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic one, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.243-250
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• 1998

In this paper, for the fuzzy constraints not only fuzziness of the constraints relation but also uncertainties of the response of the structures, allowable limits of the constraints and structural design variables, etc. are considered,. so that the fuzzy optimization of the structures can involve more wide scope of the problem and the fuzzy optimal problem is more generalized. In the decision making of the structural design scheme, every possible cases of the fuzzy variables, random variables and fuzzy-random variables, etc. for the uncertainties of the optimization problem are all considered, so the most general method of the decision making is presented. And a numerical example for the three bar truss is offered to demonstrate the reliability and execution possibility proposed method in this paper.

• Computational Structural Engineering
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• v.9 no.3
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• pp.157-167
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• 1996

A graphics system for optimum design(GOD) was developed for the various optimization programs. It is composed of a preprocessor and a postprocessor using the methods of pull-down and pop-up menus. The preprocessor of GOD system helps the designer to make a input file or a subprogram according to a selected optimization program. The postprocessor of the system display the numerical results generated during the iterative numerical analysis processes graphically in the graphic mode. Numerical examples as a mathematical linear problem and a 3-bar truss structure are presented to explain the use of GOD system. The system was programmed in one of the computer programming languages, Borland C.

• Structural Engineering and Mechanics
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• v.55 no.1
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• pp.1-27
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• 2015

This article presents a theoretical parametric analysis on the ultimate torsional behaviour of axially restrained reinforced concrete (RC) beams. This analysis is performed by using a computing procedure based on a modification of the Variable Angle Truss Model. This computing procedure was previously developed to account for the influence of the longitudinal compressive stress state due to the axial restraint conditions provided by the connections of the beams to other structural members. The presented parametric study aims to check the influence of some important variable studies, namely: torsional reinforcement ratio, compressive concrete strength and axial restraint level. From the results of this parametric study, nonlinear regression analyses are performed and some design charts are proposed. Such charts allow to correct the resistance torque of RC beams (rectangular sections with small height to width ratios) to account for the favorable influence of the axial restraint.

• Structural Engineering and Mechanics
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• v.42 no.6
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.161-167
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• 2008

In this paper, the optimal design of trusses using advanced analysis and genetic algorithm is performed. An advanced analysis takes into account geometric nonlinearity and material nonlinearity. The micro genetic algorithm is used as optimization technique. The weight of structures is treated as the objective function. The constraint functions are defined by load-carrying capacities and displacement requirement. The effectiveness of the proposed method is verified by comparing the results of the proposed method with those of other method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.9
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• pp.1399-1407
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• 2004

Structural optimization has been carried out in continuous design space or in discrete design space. Generally, available designs are discrete in design practice. However, the methods for discrete variables are extremely expensive in computational cost. An iterative optimization algorithm is proposed for design in a discrete space, which is called a sequential algorithm using orthogonal arrays (SOA). We demonstrate verifying the fact that a local optimum solution can be obtained from the process with this algorithm. The local optimum solution is defined in a discrete design space. Then the search space, which is a set of candidate values of each design variables formed by the neighborhood of a current design point, is defined. It is verified that a local optimum solution can be found by sequentially moving the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained by using the SOA algorithm

• Journal of Korean Association for Spatial Structures
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• v.1 no.1 s.1
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• pp.125-134
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• 2001

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1005-1010
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• 2004

The structural optimization has been carried out in the continuous design space or in the discrete design space. Generally, available designs are discrete in design practice. But methods for discrete variables are extremely expensive in computational cost. In order to overcome this weakness, an iterative optimization algorithm was proposed for design in the discrete space, which is called as a sequential algorithm using orthogonal arrays (SOA). We focus to verify the fact that the local solution can be obtained throughout the optimization with this algorithm. The local solution is defined in discrete design space. Then the search space, which is the set of candidate values of each design variables formed by the neighborhood of current design point, is defined. It is verified that a local solution can be founded by moving sequentially the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained using the SOA algorithm