• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.199-204
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• 2000

In this study, an optimum design algorithm using efficient reanalysis is proposed for seismic design of RC Piers. The proposed algorithm for optimization of RC Piers is based on efficient reanalysis technique. Considering structural behavior of RC Piers, several other approximation techniques, such as artificial constraint deletion is introduced to increase the efficiency of optimization. The efficiency and robustness of the proposed algorithm increase the proposed reanalysis technique is demonstrated by comparing it with a conventional optimization algorithm. A few of design examples are optimized to show the applicability of the proposed algorithm.

• Structural Engineering and Mechanics
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• v.46 no.5
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• pp.735-753
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• 2013

The wind load is always the dominant load of cooling tower due to its large size, complex geometry and thin-wall structure. At present, when computing the wind-induced response of the large-scale cooling tower, the wind pressure distribution is obtained based on code regulations, wind tunnel test or computational fluid dynamic (CFD) analysis, and then is imposed on the tower structure. However, such method fails to consider the change of the wind load with the deformation of cooling tower, which may result in error of the wind load. In this paper, the analysis of the large cooling tower based on the iterative method for wind pressure is studied, in which the advantages of CFD and finite element method (FEM) are combined in order to improve the accuracy. The comparative study of the results obtained from the code regulations and iterative method is conducted. The results show that with the increase of the mean wind speed, the difference between the methods becomes bigger. On the other hand, based on the design of experiment (DOE), an approximate model is built for the optimal design of the large-scale cooling tower by a two-level optimization strategy, which makes use of code-based design method and the proposed iterative method. The results of the numerical example demonstrate the feasibility and efficiency of the proposed method.

• Structural Engineering and Mechanics
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• v.62 no.2
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• pp.125-138
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• 2017

Reliability-based design optimization (RBDO) is a powerful tool for design optimization when considering probabilistic characteristics of design variables. However, it is often computationally intensive because of the coupling of reliability analysis and cost minimization. In this study, the concept of reliability mapping function is defined based on the relationship between the reliability index obtained by using the mean value first order reliability method and the failure probability obtained by using an improved response surface method. Double-loop involved in the classical RBDO can be converted into single-loop by using the reliability mapping function. Since the computational effort of the mean value first order reliability method is minimal, RBDO by using reliability mapping functions should be highly efficient. Engineering examples are given to demonstrate the efficiency and accuracy of the proposed method. Numerical results indicated that the proposed method has the similar accuracy as Monte Carlo simulation, and it can obviously reduce the computational effort.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.89-96
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• 2005

Numerical procedure of design optimality evaluation is studied for caisson structural systems. Two kinds of evaluation methods can be considered; mathematical optimality criteria method (MOCM) and numerical optimization method (NOM). The choice of the method depends on the available information of the system MOCM can be used only when the information of all function values, gradients and Lagrange multipliers is available, which may not be realistic in practice. Therefore, in this study, NOMs are applied for the structural optimality evaluation, where only design variables are necessary. To this end, Metropolis genetic algorithm (MGA) is advantageously used and applied for a standard optimization model of caisson composite breakwater. In the numerical example, cost and constraint functions are assumed to be changed from the orignal design situation and their effects are evaluated for optimality. From the theoretical consideration and numerical experience, it is found that the proposed optimality evaluation procedure with MGA-based NOM is efficient and practically applicable.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.127-134
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• 1999

An optimization algorithm based on Genetic Algorithm(GA) is developed for discrete optimization of reinforced concrete plane frame by constructing databases. Under multiple loading conditions, discrete optimum sets of reinforcements for both negative and positive moments in beams, their dimensions, column reinforcement, and their column dimensions are found. Construction practice is also implemented by linking columns and beams by group ‘Connectivity’between columns located in the same column line is also considered. It is shown that the developed genetic algorithm was able to reach optimum design for reinforced concrete plane frame construction practice.

• Advances in Computational Design
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• v.1 no.1
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• pp.87-103
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• 2016

This paper adopts and investigates the non-dominated sorting approach for extending the single-objective Cuckoo Search (CS) into a multi-objective framework. The proposed approach uses an archive composed of primary and secondary population to select and keep the non-dominated solutions at each generation instead of pairwise analogy used in the original Multi-objective Cuckoo Search (MOCS). Our simulations show that such a low computational complexity approach can enrich CS to incorporate multi-objective needs instead of considering multiple eggs for cuckoos used in the original MOCS. The proposed MOCS is tested on a set of multi-objective optimization problems and two well-studied engineering design optimization problems. Compared to MOCS and some other available multi-objective algorithms such as NSGA-II, our approach is found to be competitive while benefiting simplicity. Moreover, the proposed approach is simpler and is capable of finding a wide spread of solutions with good coverage and convergence to true Pareto optimal fronts.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.7
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• pp.307-314
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• 2003

This paper presents a 3D shape optimization algorithm for electromagnetic devices using the design sensitivity analysis with finite element method. The structural deformation analysis based on the deformation theory of the elastic body under stress is used for mesh renewing. The design sensitivity and adjoint variable formulae are derived for the 3D finite element method with edge element. The results of sensitivity analysis are used as the input data of the structural analysis to calculate the relocation of the nodal points. This method makes it possible that the new mesh of analysis region can be obtained from the initial mesh without regeneration. The proposed algorithm is applied to the shape optimization of 3D electromagnet pole to net a uniform flux density at the target region.

• Structural Engineering and Mechanics
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• v.53 no.6
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• pp.1127-1141
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• 2015

The optimum design of liquid column dampers in seismic vibration control considering system parameter uncertainty is usually performed by minimizing the unconditional response of a structure without any consideration to the variation of damper performance due to uncertainty. However, the system so designed may be sensitive to the variations of input system parameters due to uncertainty. The present study is concerned with robust design optimization (RDO) of liquid column vibration absorber (LCVA) considering random system parameters characterizing the primary structure and ground motion model. The RDO is obtained by minimizing the weighted sum of the mean value of the root mean square displacement of the primary structure as well as its standard deviation. A numerical study elucidates the importance of the RDO procedure for design of LCVA system by comparing the RDO results with the results obtained by the conventional stochastic structural optimization procedure and the unconditional response based optimization.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.168-173
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• 2000

Design of experiments is utilized for exploring the design space and for building response surface models in order to facilitate the effective solution of multi-objective optimization problems. Response surface models provide an efficient means to rapidly model the trade-off among many conflicting goals. In robust design, it is important not only to achieve robust design objectives but also to maintain the robustness of design feasibility under the effects of variations, called uncertainties. However, the evaluation of feasibility robustness often needs a computationally intensive process. To reduce the computational burden associated with the probabilistic feasibility evaluation, the first-order Taylor series expansions are used to derive individual mean and variance of constraints. For robust design applications, these constraint response surface models are used efficiently and effectively to calculate variances of constraints due to uncertainties. Robust optimization of automotive seat is used to illustrate the approach.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.5
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• pp.27-37
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• 2020

Structural optimization problems with discrete design variables require more function calculations (or finite element analyses) than those in the continuous design space. In this study, a method to find an optimal solution in the discrete design of the truss structure is presented, reducing the number of function calculations. Because a continuous optimal solution is the Karush-Kuhn-Tucker point that satisfies the optimality condition, it is assumed that the discrete optimal solution is around the continuous optimum. Then, response values such as weight, displacement, and stress are predicted using approximate models-referred to as hybrid metamodels-within specified design ranges. The discrete design method using the hybrid metamodels is used as a post-process of the continuous optimization process. Standard truss design problems of 10-bar, 25-bar, 15-bar, and 52-bar are solved to show the usefulness of this method. The results are compared with those of existing methods.