• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1997년도 가을 학술발표회 논문집
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• pp.533-538
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• 1997

Topology optimization of a concrete structure is discussed using an Evolutionary Structural Optimization(ESO) method introduced by Xie and Steven. During the evolution process low stressed materials are progressively removed from the structure. This paper discussed a proper rejection criterion(RC) to get a more reasonable topology of concrete structure. Some examples are presented to illustrate the optimum topology achieved by such a procedure.

• Structural Engineering and Mechanics
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• 제70권3호
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• pp.339-350
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• 2019

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.

• Structural Engineering and Mechanics
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• 제47권2호
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• pp.209-225
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• 2013

A procedure employing a Teaching-Learning Based Optimization (TLBO) method is developed to design discrete pin jointed structures. TLBO process consists of two parts: the first part represents learning from teacher and the second part illustrates learning by interaction among the learners. The results are compared with those obtained using other various evolutionary optimization methods considering the best solution, average solution, and computational effort. Consequently, the TLBO algorithm works effectively and demonstrates remarkable performance for the optimization of engineering design applications.

• 한국전산구조공학회논문집
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• 제16권3호
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• pp.281-290
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• 2003

Co Evolutionary Structural Design(CESD) Framework is presented, which can deal with the load design and structural topology design simultaneously. The load design here is the exploration algorithm that finds the critical load patterns of the given structure. In general, the load pattern is a crucial factor in determining the structural topology and being selected from the experts어 intuition and experience. However, if any of the critical load patterns would be excluded during the process of problem formation, the solution structure might show inadequate performance under the load pattern. Otherwise if some reinforcement method such as safety factor method would be utilized, the solution structure could result in inefficient conservativeness. On the other hand, the CESD has the ability of automatically finding the most critical load patterns and can help the structural solution evolve into the robust design. The CESD is made up of a load design discipline and a structural topology design discipline both of which have the fully coupled relation each other. This coupling is resolved iteratively until the resultant solution can resist against all the possible load patterns and both disciplines evolve into the solution structure with the mutual help or competition. To verify the usefulness of this approach, the 10 bar truss and the jacket type offshore structure are presented. SORA(Sequential Optimization & Reliability Assessment) is adopted in CESD as a probabilistic optimization methodology, and its usefulness in decreasing the computational cost is verified also.

• 한국농공학회논문집
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• 제49권6호
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• pp.37-46
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• 2007

In designing the structure, the stress path is the basic data. But the stress path is not standardized to analysis the structure. So the one-dimensional frame element structure model with the triangle irregular network is used to solve the problem. And the refined evolutionary structural optimization(RESO) used in structural topology optimization is applied to this study. Through this process, the search method of the stress path is advanced and the burden of the calculation. is reduced.

• 한국생산제조학회지
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• 제19권5호
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• pp.589-595
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• 2010

This paper presents a reliability-based shape optimization (RBSO) using the evolutionary structural optimization (ESO). An actual design involves uncertain conditions such as material property, operational load, poisson's ratio and dimensional variation. The deterministic optimization (DO) is obtained without considering of uncertainties related to the uncertainty parameters. However, the RBSO can consider the uncertainty variables because it has the probabilistic constraints. In order to determine whether the probabilistic constraint is satisfied or not, simulation techniques and approximation methods are developed. In this paper, the reliability-based shape design optimization method is proposed by utilization the reliability index approach (RIA), performance measure approach (PMA), single-loop single-vector (SLSV), adaptive-loop (ADL) are adopted to evaluate the probabilistic constraint. In order to apply the ESO method to the RBSO, a sensitivity number is defined as the change of strain energy in the displacement constraint. Numerical examples are presented to compare the DO with the RBSO. The results of design example show that the RBSO model is more reliable than deterministic optimization.

• Earthquakes and Structures
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• 제12권3호
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• pp.309-319
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• 2017

A new method is suggested for the retrofitting of torsionally sensitive buildings. The main objective is to eliminate the torsional component from the first two natural modes of the structure by properly modifying its stiffness distribution via selective strengthening of its vertical elements. Due to the multi-parameter nature of this problem, state-of-art optimization schemes together with an ad-hoc software implementation were used for quantifying the required stiffness increase, determine the required retrofitting scheme and finally design and analyze the required composite sections for structural rehabilitation. The performance of the suggested method and its positive impact on the earthquake response of such structures is demonstrated through benchmark examples and applications on actual torsionally sensitive buildings.

• Structural Engineering and Mechanics
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• 제76권4호
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• pp.529-540
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• 2020

Concurrent topology optimization of macrostructure and microstructure has attracted significant interest due to its high structural performance. However, most of the existing works are carried out under deterministic conditions, the obtained design may be vulnerable or even cause catastrophic failure when the load position exists uncertainty. Therefore, it is necessary to take load position uncertainty into consideration in structural design. This paper presents a computational method for robust concurrent topology optimization with consideration of load position uncertainty. The weighted sum of the mean and standard deviation of the structural compliance is defined as the objective function with constraints are imposed to both macro- and micro-scale structure volume fractions. The Bivariate Dimension Reduction method and Gauss-type quadrature (BDRGQ) are used to quantify and propagate load uncertainty to calculate the objective function. The effective properties of microstructure are evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The bi-directional evolutionary structural optimization (BESO) method is used to obtain the black-and-white designs. Several 2D and 3D examples are presented to validate the effectiveness of the proposed robust concurrent topology optimization method.

• Structural Engineering and Mechanics
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• 제72권2호
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• pp.181-190
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• 2019

Topology optimization of structures seeking the best distribution of mass in a design space to improve the structural performance and reduce the weight of a structure is one of the most comprehensive issues in the field of structural optimization. In addition to structures stiffness as the most common objective function, frequency optimization is of great importance in variety of applications too. In this paper, an efficient multi-objective Bi-directional Evolutionary Structural Optimization (BESO) method is developed for topology optimization of frequency and stiffness in continuum structures simultaneously. A software package including a Matlab code and Abaqus FE solver has been created for the numerical implementation of multi-objective BESO utilizing the weighted function method. At the same time, by considering the weaknesses of the optimized structure in single-objective optimizations for stiffness or frequency problems, slight modifications have been done on the numerical algorithm of developed multi-objective BESO in order to overcome challenges due to artificial localized modes, checker boarding and geometrical symmetry constraint during the progressive iterations of optimization. Numerical results show that the proposed Multiobjective BESO method is efficient and optimal solutions can be obtained for continuum structures based on an existent finite element model of the structures.

• 한국공작기계학회논문집
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• 제17권6호
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• pp.77-82
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• 2008

This paper presents a Reliability-Based Topology Optimization(RBTO) using the Evolutionary Structural Optimization(ESO). An actual design involves some uncertain conditions such as material property, operational load and dimensional variation. The Deterministic Topology Optimization(DTO) is obtained without considering the uncertainties related to the uncertainty parameters. However, the RBTO can consider the uncertainty variables because it has the probabilistic constraints. In order to determine whether the probabilistic constraints are satisfied or not, simulation techniques and approximation methods are developed. In this paper, the reliability index approach(RIA) is adopted to evaluate the probabilistic constraints. In order to apply the ESO method to the RBTO, sensitivity number is defined as the change in the reliability index due to the removal of the ith element. Numerical examples are presented to compare the DTO with the RBTO.