• Earthquakes and Structures
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• v.4 no.5
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• pp.527-555
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• 2013

This paper proposes probabilistic models for estimating the seismic demands on reinforced concrete (RC) bridges with base isolation. The models consider the shear and deformation demands on the bridge columns and the deformation demand on the isolation devices. An experimental design is used to generate a population of bridges based on the AASHTO LRFD Bridge Design Specifications (AASHTO 2007) and the Caltrans' Seismic Design Criteria (Caltrans 1999). Ground motion records are used for time history analysis of each bridge to develop probabilistic models that are practical and are able to account for the uncertainties and biases in the current, common deterministic model. As application of the developed probabilistic models, a simple method is provided to determine the fragility of bridges. This work facilitates the reliability-based design for this type of bridges and contributes to the transition from limit state design to performance-based design.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.04a
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• pp.135-140
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• 1993

The optimum weight design of structure is to determine the combination of structural members which minimize the weight of structures and satisfy design conditions as well. Since most of loads and design variables considered in structural design have uncertain natures, the reliability-based optimization techniques need to be developed. The aim of this study is to estabilish the general algorithm for the minimum weight design of transmission tower structure system with reliability constraints. The sequential linear programming method is used to solve non-linear minimization problems, which converts original non-linear programming problems to sequential linear programming problems. The optimal solutions are produced for various reliability levels such as reliability levels inherent in current standard transmission tower cross-section and optimal transmission tower cross-section obtained with constraints of current design criteria as well as selected target reliability index. The optimal transmission towers satisfying reliability constraints sustain consistent reliability levels on all members. Consequently, more balanced optimum designs are accomplished with less structural weight than traditional designs dealing with deterministic design criteria.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.385-390
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• 1997

In recent years there has been an increasing emphasis on shortening design cycles for bringing products to market. This requires the development of computer aided engineering tools which allow analysts to quickly evaluate the effect of design changes on noise, vibration, and harshness. Statistical Energy Analysis (SEA) modeling is a valuable tool for predicting noise and vibration as SEA models are inherently simpler and more robust than deterministic models. SEA modeling can be combined with design sensitivity analysis (DSA) to identify design changes which give the largest performance benefit. This paper describes SEA modeling of an equipment cab. SEA predictions are compared to test data, showing good agreement. The use of design sensitivity analysis in improving cab design is then demonstrated.

• Journal of Magnetics
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• v.18 no.1
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• pp.74-79
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• 2013

This paper proposes a sampling-based optimization method for electromagnetic design problems, where design sensitivities are obtained from the elaborate surrogate models based on the universal Kriging method and a local window concept. After inserting additional sequential samples to satisfy the certain convergence criterion, the elaborate surrogate model for each true performance function is generated within a relatively small area, called a hyper-cubic local window, with the center of a nominal design. From Jacobian matrices of the local models, the accurate design sensitivity values at the design point of interest are extracted, and so they make it possible to use deterministic search algorithms for fast search of an optimum in design space. The proposed method is applied to a mathematical problem and a loudspeaker design with constraint functions and is compared with the sensitivity-based optimization adopting the finite difference method.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.780-787
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• 2017

The propellant grain configuration is a design variable that determines the shape and performance of a solid rocket motor. Grain configuration variables have complicated effects on the motor performance; so the global optimization problem has to be solved in order to design the configuration variables. The grain performance has been analyzed by means of the grain burn-back and internal ballistic analysis, and the optimization technique searches for the configuration variables that satisfy the requirements. The deterministic and stochastic optimization techniques have been applied for the grain optimization, but the results are imperfect. In this study, the optimization design of the configuration variables has been performed using the hybrid optimization technique, which combines those two techniques. As a result, the hybrid optimization technique has proved to be efficient for the grain optimization design.

• 한국전산유체공학회:학술대회논문집
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• 2004.10a
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• pp.187-191
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• 2004

The conventional reliability based design optimization(RBDO) methods require high computational cost compared with the deterministic design optimization(DO) methods. To overcome the computational inefficiency of RBDO, single loop methods have been proposed. These need less function calls than that of RBDO but much more than that of DO. In this study, the approximate reliability method is proposed that the computational requirement is nearly the same as DO and the reliability accuracy is good compared with that of RBDO. Using this method, the 3-D viscous aerodynamic shape design optimization with uncertainty is performed very efficiently.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1995.10a
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• pp.30-36
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• 1995

Conventionally design for multi-stage cold forging depends on the designer's experience and decision-making. Due to such non-deterministic nature of the process sequence design, a flexible inference engine is needed for process design expert system. In this study, A* searching algorithm was introduced to arrive at the vetter process sequence design considering the number of forming stages and levels of effective strain, effective stress, and forming load during the porcess. In order to optimize the process sequence in producing the final part, cost function was defined and minimized using the proposed A* searching algorithm. For verification of the designed forming sequences, forming experiments and finite element analyses were carried out in the present investigation. The developed expert system using A* searching algorithm can produce a flexible design system based on changes in the number of forming stages and weights.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.189-194
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• 1998

Design factors are very important and deterministic in determining the first impression of products and environment. The final 30 number of channel button were chosen as a design factors at the Audio Unit. Then, we made the 8 types of prototype. with combining the design factors for experiment. Subjects rated the SD(Semantic Differential) evaluation sheets which have the 30 adjectives after watching each prototype. With the evaluated values, we simulated to identify the relation between the design factors and the adjectives using Neural Network. As a results, we could abstract the affective adjectives on each 8 types. Therefore, this research suggested the possibilities that we can infer the optimal design factors and types using Neural Network, if adjectives were given.

• International Journal of Railway
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• v.6 no.2
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• pp.45-52
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• 2013

Railway construction needs vast soil investigation for its infrastructure foundation designs along the planned railway path to identify the design parameters for stability and serviceability checks. The soil investigation data are usually classified and grouped to decide design input parameters per each construction section and budget estimates. Deterministic design method which most civil engineer and practitioner are familiar with has a clear limitation in construction/maintenance budget control, and occasionally produced overdesigned or unsafe design problems. Instead of using a batch type analysis with predetermined input parameters, data population collected from site soil investigation and design load condition can be statistically estimated for the mean and variance to present the feature of data distribution and optimized with a best fitting probability function. Probabilistic approach using entire feature of design input data enables to predict the worst, best and most probable cases based on identified ranges of soil and load data, which will help railway designer select construction method to save the time and cost. This paper introduces two Monte Carlo simulations actually applied on estimation of retaining wall external stability and long term settlement of organic soil in soil investigation area for a recent high speed railway project.

• Journal of the Earthquake Engineering Society of Korea
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• v.19 no.2
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• pp.63-74
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• 2015

This study investigated the influence of probabilistic variability in stiffness and nonlinearity of soil on response of nuclear power plant (NPP) structure subjected to seismic loads considering the soil-structure interaction (SSI). Both deterministic and probabilistic methods have been employed to evaluate the dynamic responses of the structure. For the deterministic method, $SRP_{min}$ method given in USNRC SRP 3.7.2(2013) (envelope of responses using three shear modulus profiles of lower bound($G_{LB}$), best estimate($G_{BE}$) and upper bound($G_{UB}$)) and $SRP_{max}$ method (envelope of responses by more than three ground profiles within range of $G_{LB}{\leq}G{\leq}G_{UB}$) have been considered. The probabilistic method uses the Latin Hypercube Sampling (LHS) that can capture probabilistic feature of soil stiffness defined by the median and the standard deviation. These analysis results indicated that 1) number of samples shall be larger than 60 to apply the probabilistic approach in SSI analysis and 2) in-structure response spectra using equivalent linear soil profiles considering the nonlinear behavior of soil medium can be larger than those based on low-strain soil profiles.