• Journal of Korean Society of Steel Construction
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• v.26 no.6
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• pp.523-535
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• 2014

According to the capacity design concept underlying current steel seimsic provisions, the braces in concentrically braced frames should dissipate seismic energy through cyclic tension yielding and compression buckling. On the other hand, the beams and the columns in the braced bay should remain elastic for gravity load actions and additional column axial forces resulting from the brace buckling and yielding. However, due to the difficulty in accumulating the yielding and buckling-induced column forces from different stories, empirical and often conservative approaches have been used in design practice. Recently a totally different approach was proposed by Cho, Lee, and Kim (2011) for the prediction of column axial forces in inverted V-braced frames by explicitly considering brace buckling. The idea proposed in their study is extended to X-braced seismic frames which have structural member configurations and load transfer mechanism different from those of inverted V-braced frames. Especially, a more efficient rule is proposed in combining multi-mode effects on the column axial forces by using the modal-mass based weighting factor. The four methods proposed in this study are evaluated based on extensive inelastic dynamic analysis results.

• Structural Engineering and Mechanics
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• v.78 no.1
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• pp.41-52
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• 2021

Inelastic static pushover analysis has been used in the academic-research widely for seismic analysis of structures. Nowadays, the variety pushover analysis methods have been developed, including Modal pushover, Adaptive pushover, and Cyclic pushover, in which some weaknesses of the conventional pushover method have been rectified. In the conventional pushover analysis method, the effects of cumulative growth of cracks are not considered on the reduction of strength and stiffness of RC members that occur during earthquake or cyclic loading. Therefore, the Cyclic Pushover Analysis Method (CPA) has been proposed. This method is a powerful technique for seismic evaluation of regular reinforced concrete buildings in which the first mode of them is dominant. Since the bridges have different structures than buildings, their results cannot necessarily be attributed to bridges, and more research is needed. In this study, a cyclic pushover analysis with four loading protocols (suggested by valid references) by the Opensees software was conducted for seismic evaluation of two regular reinforce concrete bridges. The modeling method was validated with the comparison of the analytical and experimental results under both cyclic and dynamic loading. The failure mode of the piers was considered in two-mode of flexural failure and also a flexural-shear failure. Along with the cyclic analysis, conventional analysis has been studied. Also, the nonlinear incremental dynamic analysis (IDA) method has been used to examine and compare the results of pushover analyses. The time history of 20 far-field earthquake records was used to conduct IDA. After analysis, the base shear vs. displacement in the middle of the deck was drawn. The obtained results show that the cyclic pushover analysis method is able to evaluate an accurate seismic behavior of the reinforced concrete piers of the bridges. Based on the results, the cyclic pushover has proper convergence with IDA. Its accuracy was much higher than the conventional pushover, in which the bridge piers failed in flexural-shear mode. But, in the flexural failure mode, the results of each two pushover methods were close approximately. Besides, the cyclic pushover method with ACI loading protocol, and ATC-24 loading protocol, can provided more accurate results for evaluating the seismic investigation of the bridges, specially if the bridge piers are failed in flexural-shear failure mode.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.2
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• pp.145-152
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• 2015

In the study, the effects of damping devices on damping ratio increase and wind-load reduction were investigated based on the computational platform, which is one of the parametric modeling methods. The computational platform helps the designers or engineers to evaluate the efficacy of the numerous alternative structural systems for irregular Super-Tall building, which is crucial in determining the capacity and the number of the supplemental damping devices for adding the required damping ratios to the building. The inherent damping ratio was estimated based on the related domestic and foreign researches conducted by using real wind-load records. Two types of damping devices were considered: One is inter-story installation type passive control devices and the other is mass type active control devices. The supplemental damping ratio due to the damping devices was calculated by means of equivalent static analysis using an equation suggested by FEMA. The optimal design of the damping devices was conducted by using the computational platform. The structural element quantity reduction effect resulting from the installation of the damping devices could be simply assessed by proposing a wind-load reduction factor, and the effectiveness of the proposed method was verified by a numerical example of a 455m high-rise building. The comparison between roof displacement and the story shear forces by the nonlinear time history analysis and the proposed method indicated that the proposed method could simply but approximately estimate the effects of the supplemental damping devices on the roof displacement and the member force reduction.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.1
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• pp.35-42
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• 2020

Torsional behavior due to the plane irregularities of the piloti building can cause excessive story drift in the torsionally outermost column, which can lead to shear failure of the column. As a seismic retrofit method that can control the torsional behavior of the piloti building, the expansion of RC wall, steel frame or steel brace may be used, but such methods may hinder the openness of the piloti floor. Therefore, in this study, linear dynamic analysis and nonlinear static analysis for piloti buildings retrofitted by knee brace were performed, and seismic performance evaluation and torsion control effect of knee brace were analyzed. The results showed that the shear force of the column increased when the piloti building retrofitted by knee brace, but it was effective in controlling the torsional deformation. In case of retrofit between knee brace and column by 30°, the shear force of the column increased less than that of 60°, and the lateral displacement of column was decreased in the order of □, ◯ and Ｈ in cross-section.

• Earthquakes and Structures
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• v.15 no.6
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• pp.667-685
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• 2018

In eccentrically braced steel frames (EBFs), the links are fuse members which enter inelastic phase before other structure members and dissipate the seismic energy. Based on the force-based seismic design method, damages and plastic deformations are limited to the links, and the main structure members are required tremendous sizes to ensure elastic with limited or no damage. Force-based seismic design method is very common and is found in most design codes, it is unable to determine the inelastic response of the structure and the damages of the members. Nowadays, methods of seismic design are emphasizing more on performance-based seismic design concept to have a more realistic assessment of the inelastic response of the structure. Links use ordinary steel Q345 (the nominal yielding strength $f_y{\geq}345MPa$) while other members use high strength steel (Q460 $f_y{\geq}460MPa$ or Q690 $f_y{\geq}690MPa$) in eccentrically braced frames with high strength steel combination (HSS-EBFs). The application of high strength steels brings out many advantages, including higher safety ensured by higher strength in elastic state, better economy which results from the smaller member size and structural weight as well as the corresponding welding work, and most importantly, the application of high strength steel in seismic fortification zone, which is helpful to popularize the extensive use of high strength steel. In order to comparison seismic behavior between HSS-EBFs and ordinary EBFs, on the basis of experimental study, four structures with 5, 10, 15 and 20 stories were designed by PBSD method for HSS-EBFs and ordinary EBFs. Nonlinear static and dynamic analysis is applied to all designs. The loading capacity, lateral stiffness, ductility and story drifts and failure mode under rare earthquake of the designs are compared. Analyses results indicated that HSS-EBFs have similar loading capacity with ordinary EBFs while the lateral stiffness and ductility of HSS-EBFs is lower than that of EBFs. HSS-EBFs and ordinary EBFs designed by PBSD method have the similar failure mode and story drift distribution under rare earthquake, the steel weight of HSS-EBFs is 10%-15% lower than ordinary EBFs resulting in good economic efficiency.

• 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.