• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.173-181
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• 2017

The obstacle deflector sweeps obstacles off the track or absorbs crash energy with an energy absorber to prevent derailment of a train and to minimize damage and casualties after an accident. In this study, an obstacle deflector and its operational mechanism were designed with a tension-type energy absorber and a 4-bar linkage system. Also, a test method was suggested and verified with FEA (Finite Element Analysis) and UTM (Universal Test Machine) for testing of the static load and energy absorbing ability according to EN 15227 regulations. Through this study, an obstacle deflector that meets the EN 15227 standard was designed and a test method was suggested to adjust the collapse load easily and to verify it experimentally according to the design and verification procedure of the obstacle deflector.

• Journal of the Earthquake Engineering Society of Korea
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• v.19 no.1
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• pp.29-36
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• 2015

Base isolation is considered as a seismic protective system in the design of next generation Nuclear Power Plants (NPPs). If seismic isolation devices are installed in nuclear power plants then the safety under a seismic load of the power plant may be improved. However, with respect to some equipment, seismic risk may increase because displacement may become greater than before the installation of a seismic isolation device. Therefore, it is estimated to be necessary to select equipment in which the seismic risk increases due to an increase in the displacement by the installation of a seismic isolation device, and to perform research on the seismic performance of each piece of equipment. In this study, modified NRC-BNL benchmark models were used for seismic analysis. The numerical models include representations of isolation devices. In order to validate the numerical piping system model and to define the failure mode, a quasi-static loading test was conducted on the piping components before the analysis procedures. The fragility analysis was performed by using the results of the inelastic seismic response analysis. Inelastic seismic response analysis was carried out by using the shell finite element model of a piping system considering internal pressure. The implicit method was used for the direct integration time history analysis. In addition, the collapse load point was used for the failure mode for the fragility analysis.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.5
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• pp.151-159
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• 2000

Dynamic loading of structures often causes excursions of stresses will into the inelastic range and the influence of geometry changes on the response is also significant in may cases. In general , the shell structures designed according to quasi-Static analysis may collapse under condition of dynamic loading. Therefore, for a more realistic prediction on the lad carrying capacity of these shell. both material and geometric nonlinear effects should be considered. In this study , the material nonlinearity effect on the dynamic response is formulated by the elasto-viscoplastic model highly corresponding to the real behavior of the material. Also, the geometrically nonlinear behavior is taken into account using a Total Lagrangian formulation. the reinforcing bars are modeled by the equivalent steel layer at the location of reinforcements, and Von Mises yield criteria is adopted for the steel layer behavior. Also, Drucker-Prager yield criteria is applied for the behavior of concrete. the shape imperfection of dome is assumed as 'dimple type' which can be expressed Wd1=Wd0(1-(r-a)m)n while the shape imperfection of wall is assumed as sinusoidal curve which is Wwi =Wwo sin(n $\pi$y/l). In numerical test, three cases of shape imperfection of 0.0 -5.0cm(opposite direction to loading ; inner shape imperfection)and 5cm (direction to loading : outward shape imperfection) and thickness of steel layer determined by steel ratio of 0,3, and 5% were analyzed. The effect of shape imperfection and steel ratio and behavior characteristics of perfect shape shell and imperfect shape shell are identified through analysis of above mentioned numerical test. Dynamic behaviors of dome and wall according toe combination of shape imperfection and steel ratio are also discussed in this paper.

• Steel and Composite Structures
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• v.43 no.2
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• pp.139-152
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• 2022

Steel-reinforced concrete (SRC) L-shaped column is the vertical load-bearing member with high spatial adaptability. The seismic behavior of SRC L-shaped column is complex because of their irregular cross sections. In this study, the hysteretic performance of six steel truss reinforced concrete L-shaped columns specimens under the combined loading of compression, bending, shear, and torsion was tested. There were two parameters, i.e., the moment ratio of torsion to bending (γ) and the aspect ratio (column length-to-depth ratio (φ)). The failure process, torsion-displacement hysteresis curves, and bending-displacement hysteresis curves of specimens were obtained, and the failure patterns, hysteresis curves, rigidity degradation, ductility, and energy dissipation were analyzed. The experimental research indicates that the failure mode of the specimen changes from bending failure to bending-shear failure and finally bending-torsion failure with the increase of γ. The torsion-displacement hysteresis curves were pinched in the middle, formed a slip platform, and the phenomenon of "load drop" occurred after the peak load. The bending-displacement hysteresis curves were plump, which shows that the bending capacity of the specimen is better than torsion capacity. The results show that the steel truss reinforced concrete L-shaped columns have good collapse resistance, and the ultimate interstory drift ratio more than that of the Chinese Code of Seismic Design of Building (GB50011-2014), which is sufficient. The average value of displacement ductility coefficient is larger than rotation angle ductility coefficient, indicating that the specimen has a better bending deformation resistance. The specimen that has a more regular section with a small φ has better potential to bear bending moment and torsion evenly and consume more energy under a combined action.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.2
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• pp.19-30
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• 2012

Due to the fact that the social environment is improved and the urban development is stabilized, the demand of new construction of apartment becomes slowdown. Accordingly, there are many researches to lengthen the service life of the existing apartment through the remodeling and its importance is continuously rising. However, reliable design specifications and guidelines for the design of remodeling with partial demolition are not provided yet in Korea. Specially, in the apartment remodeling, slab collapse accidents take major portion in all accidents that reported by Korean Government. It is very important to prevent intial crack of slab because intial crack could cause severe accident like collapse of all structure in a short period of time. The purpose of this study is to develop structural guidelines that could guarantee the structural safety and serviceability of slab structure and could be adopted in Korean remodeling with partial demolition. There are mainly two components to determine structural behavior of slab structure. One is the shape of slab structure and the other is load which is resisted by the slab structure. In this study, the weight per unit volume of concrete debris and concrete strength are estimated through the analysis of previous researches to recognize the relationship between the shape of slab and load that loaded on the slab. Accordingly, approximately 300 pieces of floor plan are collected and analyzed. The finite element analysis is conducted using these analyzed and estimated results. From the finite element analysis results, the limited stacking height of debris is suggested and the stacking method is also discussed. In addition, to find the relationship between movement of demolition equipment and structural behavior of slab, the static and dynamic loading tests are conducted. From the results of loading tests, the impact factor which will be considered in the remodeling design could be estimated.

• Journal of the Korea Concrete Institute
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• v.16 no.2 s.80
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• pp.249-260
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• 2004

The longitudinal steel connection of reinforced concrete bridge column is sometimes practically unavoidable, however the current Korean bridge design specifications have no special provisions about lap-splices of longitudinal steel. This paper reports experimental results of a research program investigating the seismic performance of circular RC bridge columns with respect to longitudinal steel connection detailing. Twenty-one circular column specimens were tested under quasi-static test. The columns with the entire longitudinal steel lap-spliced within plastic hinge region show relatively sudden strength degradation and low ductility than the columns with continuous longitudinal steel and the columns with half of longitudinal steel lap-spliced. However, the seismic performance of the column with mechanically connected longitudinal steel is similar to that of the column with continuous longitudinal steel. The final objectives of this study are to suggest appropriate longitudinal reinforcement connection details for the limited ductility design concept and to provide quantitative reference data and tendency for performance or damage assessment based on the performance levels such as cracking, yielding, collapse, etc. Ultimate displacement/drift ratio, displacement ductility, response modification factor, equivalent viscous damping ratio, residual deformation index, and effective stiffness are investigated and discussed in this paper.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.