• Structural Engineering and Mechanics
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• v.52 no.2
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• pp.261-274
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• 2014

An accurate finite element (FE) model of a structure is essential for predicting reliably its dynamic characteristics. Such a model is used to predict the effects of structural modifications for dynamic design of the structure. These modifications may be imposed by design alterations for operating reasons. Most of the model updating techniques neglect damping and so these updated models can't be used for accurate prediction of vibration amplitudes. This paper deals with the basic formulation of damped finite element model updating method and its use for structural dynamic modifications. In this damped damped finite element model updating method, damping matrices are updated along with mass and stiffness matrices. The damping matrices are updated by updating the damping coefficients. A case involving actual measured data for the case of F-shaped test structure, which resembles the skeleton of a drilling machine is used to evaluate the effectiveness of damped FE model updating method for accurate prediction of the vibration levels and thus its use for structural dynamic modifications. It can be concluded from the study that damped updated FE model updating can be used for structural dynamic modifications with confidence.

• Structural Engineering and Mechanics
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• v.55 no.3
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• pp.525-534
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• 2015

A new structural dynamic fuzzy reliability analysis under stochastic loads which are applied several times is proposed in this paper. The fuzzy reliability prediction models based on time responses with and without strength degeneration are established using the stress-strength interference theory. The random loads are applied several times and fuzzy structural strength is analyzed. The efficiency of the proposed method is demonstrated numerically through an example. The results have shown that the proposed method is practicable, feasible and gives a reasonably accurate prediction. The analysis shows that the probabilistic reliability is a special case of fuzzy reliability and fuzzy reliability of structural strength without degeneration is also a special case of fuzzy reliability with structural strength degeneration.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.401-406
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• 1998

This study aimed at the technique for the construction control in braced excavation works. Selecting a case of Kwangju subway works, field observational values were compared with prediction using Plaxis's. Maximum observational values relevant to both horizontal and vertical proved satisfactory in that they were within the criteria. Numerical results by used Plaxis were overestimated greater than observational values, which meant the prediction were safe tendency. .It must be emphasized that displacement measurement for neighboring important structures should be carried out in order to take countermeasure charge for construction methods, in case that the risk or failure was previewed.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.3
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• pp.42-54
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• 2010

The purpose of this study is to analyze the aircraft icing index of Korean peninsula using the numerical weather prediction model, KWRF and pilot weather report data. As comparing the pilot weather report data with the calculated icing index using the KWRF model result, SCLW, RAP, and AFGWC index are more useful than any other index, and IC2, NAWAU, and RSID index are different case by case. But IC1, SID1 and SID2 index show that these overestimated severe icing in every vertical level. Through this icing study, it is expected that this study will help to develop the proper icing index of Korean peninsula.

• Journal of the Korean Society of Safety
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• v.24 no.3
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• pp.88-95
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• 2009

Most of subway stations are located underground and the number of passengers is far more than that of designed value, therefore the risk of accident is growing bigger and serious damage is expected in case of disaster. In Korea the period of evacuation study is short and numerical and experimental data of evacuation phenomena in subway station is rare. Many egress evaluation depend on foreign commercial S/Ws which are not yet proven its availability in special case such as subway station. In this paper outflow coefficients which are essential in egress evaluation are calculated at train door, stairway and turnstile at 3 most crowed subway stations. This numerical data can be used in prediction of egress evaluation and the result of other prediction methods can be verified with these experimental data.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.151-165
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• 2006

It is important to estimate the true misclassification rate of a given classifier when an independent set of test data is not available. Cross-validation and bootstrap are two possible approaches in this case. In related literature bootstrap estimators of the true misclassification rate were asserted to have better performance for small samples than cross-validation estimators. We compare the two estimators empirically when the classification rule is so adaptive to training data that its apparent misclassification rate is close to zero. We confirm that bootstrap estimators have better performance for small samples because of small variance, and we have found a new fact that their bias tends to be significant even for moderate to large samples, in which case cross-validation estimators have better performance with less computation.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.523-528
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• 2008

The accurate distribution of flow rate has been a very important part to control the air change rate since introduction of house ventilation system. An inappropriate selection of fan due to incorrect prediction of pressure loss in duct brings energy loss. In the previous study the pressure loss of general spiral duct was measured and database was constructed for finding correct loss factors in fitting upper stream. The purpose of this study is to compare and investigate the error range of flow rate by applying T-Method to bilateral symmetry and asymmetry layout of duct. The results of this study are as following. It is demanded to decide accurate size under duct design for house ventilation system. Because the small amount of Flow rate was considered at that time. The error range was 3.17% on case1 and 3.52% on case2. The error range difference was 0.35%.

• Journal of Welding and Joining
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• v.32 no.2
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.270-275
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• 2007

Pollutants emitted by Diesel trains seriously affected on the ventilation characteristics in the subway or railway tunnel network systems. Thus, numerical prediction results and analyses are required to engineers who should design ventilation facilities. So here, this paper presents some numerical results about the ventilation characteristics, such as pollutants distributions and a suitability of the ventilation facilities pre-designed in case that diesel trains use the same railway track with normal passenger trains

• Earthquakes and Structures
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• v.16 no.6
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• pp.679-690
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• 2019

This paper presents machine learning methods using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) to analyze optimal damper distribution for structural vibration control. Regarding different building structures, a genetic algorithm based optimization method is used to determine optimal damper distributions that are further used as training samples. The structural features, the objective function, the number of dampers, etc. are used as input features, and the distribution of dampers is taken as an output result. In the case of a few number of damper distributions, multi-class prediction can be performed using SVM and MLP respectively. Moreover, MLP can be used for regression prediction in the case where the distribution scheme is uncountable. After suitable post-processing, good results can be obtained. Numerical results show that the proposed method can obtain the optimized damper distributions for different structures under different objective functions, which achieves better control effect than the traditional uniform distribution and greatly improves the optimization efficiency.