• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.387-394
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• 2017

Seismic probabilistic risk assessment (SPRA) requires a large number of simulations to evaluate the seismic vulnerability of structural and nonstructural components in nuclear power plants. The effect of structural modeling and analysis assumptions on dynamic analysis of 3D and simplified 2D stick models of auxiliary buildings and the attached nonstructural components is investigated. Dynamic characteristics and seismic performance of building models are also evaluated, as well as the computational accuracy of the models. The presented results provide a better understanding of the dynamic behavior and seismic performance of auxiliary buildings. The results also help to quantify the impact of uncertainties associated with modeling and analysis of simplified numerical models of structural and nonstructural components subjected to seismic shaking on the predicted seismic failure probabilities of these systems.

• Structural Engineering and Mechanics
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• v.19 no.2
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• pp.217-230
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• 2005

A steel silo traditionally consists of a cylindrical and a conical shell. In order to facilitate emptying operations, the cylinder is placed on local supports. This may lead to dangerous stress concentrations and eventually to local instability of the cylindrical wall. In this contribution, the locally supported cylinder is strengthened by means of ring stiffeners and longitudinal stiffeners and the effect of their dimensions on the buckling stress is investigated. This study leads to a number of diagrams, each of them representing the effect of one of the dimensions on the buckling stress. In each diagram, the failure pattern corresponding to the buckling stress is indicated.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1991.04a
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• pp.20-25
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• 1991

In order to predict properly the effects of ground motion associated wi th earthquakes on underground radioactive waste disposal facilities, understanding of the structural behavior of an underground opening in discontinuous rook masses subjected to dynamic loadings is essential. Therefore, this paper includes literature review on computational models for discontinuous rook masses and on mathematical models for the structural analysis of underground openings. Then, structural analyses of underground openings using the distinct element computer program written for the static and dynamic analysis of discontinuous rook masses have been performed.

• Environmental and Resource Economics Review
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• v.20 no.3
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• pp.419-457
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• 2011

This study aims at understanding the characteristics of global economic models, which are widely used for climate change policy analysis. A literature review study was conducted in order to derive general features of top-down models such as CGE and bottom-up/hybrid models such as GTEM. Furthermore, a structural analysis was carried out by applying parameter and structural components from other models to a particular model to observe the potential differences in outcomes. Literature review shows that bottom-up or hybrid models generally have higher level of reduction potentials than top-down models in the long run. This contradicts the conclusion presented by IPCC, and raises the need for more rigorous investigation through structural analysis. Structural analysis of EPPA model indicates that the structural component of the energy sector in a particular model is the most influential factor in predicting baseline emissions and reduction potentials. This includes the structure among energy, capital, and labor inputs, and the substitution elasticities within the energy bundle. Technology bundle can establish the conclusions from literature review, and change in Armington elasticities do not significantly affect the outcome in aggregate.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.725-732
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• 2006

This study has been started for the development of a refined live load distribution formula that has safety and precision toward I type prestressed concrete girder bridge. This type of bridge is mainly applied to short span bridges that are $25{\sim}40m$ in length. Based on various structure analysis models that are currently being applied as preceding studies for the development of live load distribution method. an analysis of flexural stiffness ratio for barrier and diaphragm has been performed. As the result of parametric analysis for the changes in flexural stiffness ratio, the effect of barrier on load distribution showed as insignificant in all structural analysis models while analyzing the deflection distribution. Also. the deflection distribution of the models with stiffness of 25% in which the diaphragm eccentricity is accounted for as same as the models with stiffness of 100% in which the diaphragm eccentricity is unaccounted for. This results are verified through the comparison with a experimental data.

• Journal of the Korea Society for Simulation
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• v.6 no.1
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• pp.1-13
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• 1997

In this paper, we present a partitioning algorithm for distributed simulation of DEVS (Discrete Event System Specification) models. To preserve concurrency inherent in models, the proposed algorithm utilizes the structural information of models. Through benchmark simulation experiments, we show that the proposed algorithm can generate good partitions.

• Structural Engineering and Mechanics
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• v.7 no.3
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• pp.303-318
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• 1999

Cracking of reinforced concrete flexural members is a highly random phenomenon. In this paper reliability models are presented to determine the probabilities of failure of flexural members against the limit states of first crack and maximum crackwidth. The models proposed take into account the mechanism of cracking. Based on the reliability models discussed, Eqs. (8) and (9) useful in the reliability-based design of flexural members are presented.

• Computers and Concrete
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• v.13 no.2
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• pp.271-290
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• 2014

In this study, structural irregularities in plan, which has a considerable effect on earthquake behavior of buildings, have been investigated in detail based on Turkish Earthquake Code 2007. The study consists of six main parametric models and a total of 144 sub-models that are grouped based on RC structural systems such as frame, frame + rigid core, frame with shear wall, and frame with shear wall + rigid core. All models are designed to have both symmetrical plan geometry and regular rigidity distribution. Changes in the earthquake behavior of buildings were evaluated according to the number of storeys, number of axes and the configuration of structural elements. Many findings are obtained and assessed as a result of the analysis for each structural irregularity. The study shows that structural irregularities can be observed in completely symmetric buildings in terms of plan geometry and rigidity distribution.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.