• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.2
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• pp.9-22
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• 2008

This paper presents an integrated optimal design technique of a hybrid structure-damper system for improving the seismic performance of the structure. The proposed technique corresponds to the optimal distribution of the stiffness and dampers. The multi-objective optimization technique is introduced to deal with the optimal design problem of the hybrid system, which is reformulated into the multi-objective optimization problem with a constraint of target reliability in an efficient manner. An illustrative example shows that the proposed technique can provide a set of Pareto optimal solutions embracing the solutions obtained by the conventional sequential design method and single-objective optimization method based on weighted summation scheme. Based on the stiffness and damping capacities, three representative designs are selected among the Pareto optimal solutions and their seismic performances are investigated through the parametric studies on the dynamic characteristics of the seismic events. The comparative results demonstrate that the proposed approach can be efficiently applied to the optimal design problem for improving the seismic performance of the structure.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1071-1079
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• 2018

In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.

• Journal of Power System Engineering
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• v.21 no.1
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• pp.5-10
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• 2017

Pitch drive system in wind turbine is composed by the planetary gear system to satisfied its required performance such as long life and light weight for gear train. When the planetary gear system can reduce its volume and weight, the power consumption of the wind turbine can be reduced. In this study, the planetary gear system of the pitch drive system in medium-sized wind turbine is obtained for weight reduction by shape optimization method. And the planetary gear system is verified for their strength by the structural analysis.

• Structural Engineering and Mechanics
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• v.52 no.6
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• pp.1209-1224
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• 2014

In Current paper, the voltages of patches optimization are carried out for minimizing the power consumption of piezoelectric patches and maximum vertical displacement of symmetrically FML panels using the modified multi-objective Elitist-Artificial Bee Colony (E-ABC) algorithm. The voltages of patches, panel length/width ratios, ply angles, thickness of metal sheets and edge conditions are chosen as design variables. The classical laminated plate theory (CLPT) is considered to model the transient response of the panel, and numerical results are obtained by the finite element method. The performance of the E-ABC is also compared with the PSO algorithm and shows the good efficiency of the E-ABC algorithm. To check the validity, the transient responses of isotropic and orthotropic panels are compared with those available in the literature and show a good agreement.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.3
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• pp.319-323
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• 2012

Transmission is one of the important pars to transmit power from engine to wheels. Mass reduction gears can make the engine power requirement reduce, and can make dynamic performance and fuel efficiency of vehicle improve. Transmission gears are modified for mass reduction without changing their tooth shapes, face widths, and modules by using shape optimization and re-check process. Also structural stability is verified by FEA.

• Journal of Korean Association for Spatial Structures
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• v.3 no.3 s.9
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• pp.75-83
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• 2003

The purpose of this study is to obtain the optimum shape of cable domes by using the real coding genetic algorithm. Generally, the structural performance of the cable domes is influenced very sensitively by pre-stress, geometry and length of the mast because of flexible system. So, it is very important to decide the optimum shape to get maximum stiffness of cable domes. We use the analytical model to verify the usefulness of this algorithm for shape optimization and analyze the roof system of Seoul olympic gymnastic arena as analytical model of a practical structures. It is confirmed lastly that the optimum shape domes have more stiffness than initial shape ones.

• Structural Engineering and Mechanics
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• v.57 no.1
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• pp.161-178
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• 2016

This research is aimed to design and analyze the performance of double dynamic vibration absorber (DVA) using a pendulum and a spring-mass type absorber for reducing vibration of two-DOF vibration system. The conventional fixed-points method and genetics algorithm (GA) optimization procedure are utilized in designing the optimal parameter of DVA. The frequency and damping ratio are optimized to determine the optimal absorber parameters. The simulation results show that GA optimization procedure is more effective in designing the double DVA in comparison to the fixed-points method. The experimental study is conducted to verify the numerical result.

• Structural Engineering and Mechanics
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• v.78 no.1
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• pp.77-86
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• 2021

Local school buildings are critical facilities that can provide shelter in disasters such as earthquakes, so they must be more resistant to seismic forces than other structures. In this study, a sensitivity analysis was conducted to determine which columns-as the most critical members in a reinforced concrete building-most urgently require seismic retrofitting. The sensitivity analysis was conducted using an optimization technique with the location of each column as a parameter. A numerical model was developed to simulate a realistic collapse mode through a three-dimensional dynamic analysis. Based on numerical analysis results, it was found that the columns positioned in the lower floors, such as the first floor and in the outer part of a building, urgently require retrofitting. For reinforcement of the RC columns, which has been proven for its performance in previous research, was applied. Through this study, the importance of appropriate retrofitting is demonstrated. Further, a method for determining the appropriate location for retrofitting-when retrofitting is not possible on the entire structure-is presented.

• Earthquakes and Structures
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• v.23 no.4
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• pp.329-338
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• 2022

This work addresses the optimization controller design problem combining the AI evolution bat (EB) optimization algorithm with a fuzzy controller in the practical application of a reinforced concrete frame structure. This article explores the use of an intelligent EB strategy to reduce the dynamic response of Lead Rubber Bearing (LRB) composite reinforced concrete frame structures. Recently developed control units for plant structures, such as hybrid systems and semi-active systems, have inherently non-linear properties. Therefore, it is necessary to develop non-linear control methods. Based on the relaxation method, the nonlinear structural system can be stabilized by properly adjusting the parameters. Therefore, the behavior of a closed-loop system can be accurately predicted by determining the behavior of a closed-loop system. The performance and durability of the proposed control method are demonstrated by numerical simulations. The simulation results show that the proposed method is a viable and feasible control strategy for seismically tuned composite reinforced concrete frame structures.