• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.677-689
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• 2022

Generally, the goal of seismic retrofit design of an existing structure using energy dissipation devices is to determine the optimum design parameters of a retrofit device to satisfy a specified limit state with minimum cost. However, the presence of multiple parameters to be optimized and the computational complexity of performing non-linear analysis make it difficult to find the optimal design parameters in the realistic 3D structure. In this study, genetic algorithm-based optimal seismic retrofit methods for determining the required number, yield strength, and location of steel slit dampers are proposed to retrofit an asymmetric soft first-story structure. These methods use a multi-objective and single-objective evolutionary algorithms, each of which varies in computational complexity and incorporates nonlinear time-history analysis to determine seismic performance. Pareto-optimal solutions of the multi-objective optimization are found using a non-dominated sorting genetic algorithm (NSGA-II). It is demonstrated that the developed multi-objective optimization methods can determine the optimum number, yield strength, and location of dampers that satisfy the given limit state of a three-dimensional asymmetric soft first-story structure. It is also shown that the single-objective distribution method based on minimizing plan-wise stiffness eccentricity turns out to produce similar number of dampers in optimum locations without time consuming nonlinear dynamic analysis.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.625-632
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• 2012

Optimization of process parameters in polymer extrusion is an important task to reduce manufacturing cost. To determine the optimum values of the process parameters, it is essential to find their influence on the strength of polymer breathable thin film. The significance of six important process parameters namely, extruder cylinder temperature, extruder speed, extruder dies temperature, cooling roll temperature, stretching ratio, stretching roll temperature on breathable film strength of polymer extrusion was determined. Moreover, this paper presents the application of Taguchi method and analysis of variance (ANOVA) for maximization of the breathable film strength influenced by extrusion parameters. The optimum parameter combination of extrusion process was obtained by using the analysis of signal-to-noise ratio. The conclusion revealed that extruder speed and stretching ratio were the most influential factor on the film strength, respectively. The best results of film strength were obtained at higher extruder speed and stretching ratio.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.3
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• pp.241-245
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• 2001

In this paper, we present parameter optimization technique for GaAs/AlGaAs multiple quantum well avalanche photodiodes used for image capture mechanism in high-definition system. Even under flawless environment in semiconductor manufacturing process, random variation in process parameters can bring the fluctuation to device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. This paper will first use experimental design and neural networks to model the nonlinear relationship between device process parameters and device performance parameters. The derived model was then put into genetic algorithms to acquire optimized device process parameters. From the optimized technique, we can predict device performance before high-volume manufacturign, and also increase production efficiency.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.04a
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• pp.139-146
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• 1997

The main purpose of this paper is to investigate the near-optimal parameters of continuous beam subject to a moving load. The computer-aided optimization technique is used to obtain the near-optimal parameters. The computer program is developed to obtain the natural frequency parameters and the forced vibration responses to a transit point load for the continuous beam with variable support spacing, mass and stiffness. The optimization function to describe the design efficiency is defined as a linear combination of four dimensionless span characteristics: the maximum dynamic stress; the stress difference between span segments; the rms deflection under the transit point load; and the total span mass. Studies of three span beams show that the beam with near-optimal parameters can improve design efficiency by 12 to 24 percent when compared to a reference configuration beams of the same total span length.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.223-227
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• 2002

Wind tunnel test or CFD is used for predicting aerodynamic drag coefficient in domestic motor companies. But, wind tunnel test requires much cost and time, and CFD has a relatively large error. In this study a predicting program of the aerodynamic drag coefficient based on empirical techniques was developed. Also GRG method was added to the program in order to decide optimal values of some parameters. The program was applied to 24 cars and the aerodynamic drag coefficients were predicted with 4.82% average error. Optimization was also accomplished to 6 cars. Some parameters to be modified were determined (1) to reduce the afterbody drag coefficient to the value established by a designer and (2) to preserve the same drag coefficient as the original automotive when some parameters have to be changed in the viewpoint of design. It was verified that the developed program can predict the aerodynamic drag coefficient appropriately and determine optimal values of some parameters.

• Radiation Oncology Journal
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• v.10 no.1
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• pp.95-100
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• 1992

Since LINAC-based stereotactic radiosurgery uses multiple noncoplanar arcs, three-dimensional dose evaluation and many beam parameters, a lengthy computation time is required to optimize even the simplest case by a trial and error. The basic approach presented in this paper is to show promising methods using an experimental optimization and an analytic optimization The purpose of this paper is not to describe the detailed methods, but introduce briefly, proceeding research done currently or in near future. A more detailed description will be shown in ongoing published papers. Experimental optimization is based on two approaches. One is shaping the target volumes through the use of multiple isocenters determined from dose experience and testing. The other method is conformal therapy using a beam's eye view technique and field shaping. The analytic approach is to adapt computer-aided design optimization in finding optimum irradiation parameters automatically.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.87-92
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• 2016

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.337-348
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• 2023

The optimization of reinforced concrete (RC) cantilever retaining walls is a complex problem and requires the use of advanced techniques like metaheuristic algorithms. For this purpose, an optimization model must first be developed, which involves mathematical complications, multidisciplinary knowledge, and programming skills. This task has proven to be too arduous and has halted the mainstream acceptance of optimization. Therefore, it is necessary to unravel the complications of optimization into an easily applicable form. Currently, the most commonly used method for designing retaining walls is by following the proportioning limits provided by the ACI handbook. However, these limits, derived manually, are not verified by any optimization technique. There is a need to validate or modify these limits, using optimization algorithms to consider them as optimal limits. Therefore, this study aims to propose updated proportioning limits for the economical design of a RC cantilever retaining wall through a comprehensive parametric investigation using the genetic algorithm (GA). Multiple simulations are run to examine various design parameters, and trends are drawn to determine effective ranges. The optimal limits are derived for 5 geometric and 3 reinforcement variables and validated by comparison with their predecessor, ACI's preliminary proportioning limits. The results indicate close proximity between the optimized and code-provided ranges; however, the use of optimal limits can lead to additional cost optimization. Modifications to achieve further optimization are also discussed. Besides the geometric variables, other design parameters not covered by the ACI building code, like reinforcement ratios, bar diameters, and material strengths, and their effects on cost optimization, are also discussed. The findings of this investigation can be used by experienced engineers to refine their designs, without delving into the complexities of optimization.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.4
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• pp.497-503
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• 2012

The aim of this study was to optimize S-tube shape of heat exchanger in term of reducing the size of tube bundle and improving the mechanical properties such as the thermal stress and resonance. The geometric parameters such as offset length, the straight distance between one end and other end of tube, the tube length in straight portion and fillet radius was assessed as a valid parameters. The structural analysis was performed to estimate the structural characteristics. Main effect analysis was performed to investigate the main effect for the various geometric parameters. The response surface methodology was employed to establish mathematical approximation models as a function of the geometric parameters of the S-tube. Also, The optimization was performed to optimize geometric parameters of S-tube using the regression equations and optimization tool. The optimized tube shape has been proposed. Those could be used in the heat exchanger design used in high temperature.