• Structural Engineering and Mechanics
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• v.54 no.3
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• pp.501-520
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• 2015

Presence of torsional loadings can significantly affect the flow of internal forces and deformation capacity of reinforced concrete (RC) columns. It increases the possibility of brittle shear failure leading to catastrophic collapse of structural members. This necessitates accurate prediction of the torsional behaviour of RC members for their safe design. However, a review of previously published studies indicates that the torsional behaviour of RC members has not been studied in as much depth as the behaviour under flexure and shear in spite of its frequent occurrence in bridge columns. Very few analytical models are available to predict the response of RC members under torsional loads. Softened truss model (STM) developed in the University of Houston is one of them, which is widely used for this purpose. The present study shows that STM prediction is not sufficiently accurate particularly in the post cracking region when compared to test results. An improved analytical model for RC square columns subjected to torsion with and without axial compression is developed. Since concrete is weak in tension, its contribution to torsional capacity of RC members was neglected in the original STM. The present investigation revealed that, disregard to tensile strength of concrete is the main reason behind the discrepancies in the STM predictions. The existing STM is extended in this paper to include the effect of tension stiffening for better prediction of behaviour of square RC columns under torsion. Three different tension stiffening models comprising a linear, a quadratic and an exponential relationship have been considered in this study. The predictions of these models are validated through comparison with test data on local and global behaviour. It was observed that tension stiffening has significant influence on torsional behaviour of square RC members. The exponential and parabolic tension stiffening models were found to yield the most accurate predictions.

• Journal of the Korean Geosynthetics Society
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• v.18 no.4
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• pp.129-138
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• 2019

In this study, the rebar rock bolt sensor and GFRP rock bolt sensor, which can be monitored, were embedded in a large model slope, and the behavior of slopes occurred in the early stage of slope collapse was analyzed after performing the field failure test, numerical analysis of the individual element method and finite element method. By comparing and analyzing the field test and numerical analysis results, field applicability of rock slope collapse monitoring on the rebar rock bolt sensor and GFRP rock bolt sensor was investigated. Through this study, smart slope collapse prediction and warning system was developed, which can be used to induce effective evacuation of residents living in the collapsible area by detecting landslide and ground decay precursor information in advance.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.19-26
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• 2002

In this paper, the nonlinear structural analysis for the composite structure of the spent nuclear fuel disposal container and the 50cm thick bentonite buffer is carried out to predict the collapse of the container while the sudden rock movement of 10cm is applied on the composite structure. This sudden rock movement is anticipated by the earthquake etc. at a deep underground. Horizontal symmetric rock movement is assumed in this structural analysis. Elastoplastic material model is adopted. Drucker-Prager yield criterion is used for the material yield prediction of the bentonite buffer and von-Mises yield criterion is used for the material yield prediction of the container(cast iron insert, copper outer shell and lid and bottom). Analysis results show that even though very large deformations occur beyond the yield point in the bentonite buffer, the container structure still endures elastic small strains and stresses below the yield strength. Hence, the 50cm thick bentonite buffer can protect the container safely against the 10cm sudden rock movement by earthquake etc.. Analysis results also show that bending deformations occur in the container structure due to the shear deformation of the bentonite buffer. The elastoplastic nonlinear structural analysis for the composite structure of the container and the bentonite buffer is performed using the finite element analysis code, NISA.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.287-295
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• 2023

The collapse of bridges due to earthquakes results in many casualties and property damages. Thus, accurate prediction and preparation are required for the behavior of bridges during earthquakes. In particular, columns play an important role in the seismic behavior of bridges. The risk of collapse due to an earthquake increases when there is a problem of the insufficient lap splice in the column. In this study, to analyze the characteristics of the lap splice in the column, a numerical model was defined for the insufficient lap-spliced columns and verified using experimental data. The developed column model was applied to a commonly used RC slab bridge. Nonlinear static analysis for the column was performed to evaluate the change in the performance of the column according to the lap-spliced length. In addition, this study assessed the effect of the lap-spliced length on the seismic fragility analysis.

• Transactions of Materials Processing
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• v.12 no.5
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• pp.479-486
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• 2003

This paper presents an elastoplastic analysis for spent nuclear fuel disposal container and its 50 cm thick bentonite buffer to predict the collapse of the container while the horizontal asymmetric sudden rock movement of 10 cm is applied on the composite structure. This sudden rock movement is anticipated by the earthquake etc. at a deep underground. Elastoplastic material model is adopted. Drucker-Prager yield criterion is used for the material yield prediction of the bentonite buffer and von-Mises yield criterion is used for the material yield prediction of the container. Analysis results show that even though very large deformations occur beyond the yield point in the bentonite buffer, the container structure still endures elastic small strains and stresses below the yield strength. Hence, the asymmetric 50 cm thick bentonite buffer can protect the container safely against the 10 cm sudden rock movement by earthquake etc.. Analysis results also show that bending deformations occur in the container structure due to the shear deformation of the bentonite buffer. The finite element analysis code, NISA, is used for the analysis.

• Aerospace Engineering and Technology
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• v.10 no.1
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• pp.54-62
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• 2011

The pressurant mass required for propellant tank pressurization with operating condition variation was estimated by using the numerical model already developed for this purpose. The model was applied to the concept design results of KSLV-II first stage oxygen tank. The supplied pressurant temperature, oxygen volumetric flow rate, and the ratio of length to diameter of the tank were selected as variables. The required pressurant mass and mass flow rate, collapse factor, ullage temperature distribution were predicted, and the results showed that the pressurant temperature had the largest effect on the amount of the required pressurant mass. The pressurizing efficiency of the propellant tank was calculated through analyzing energy distribution in the ullage. It was found that the gas-to-wall heat transfer in the ullage was dominant, and much of the pressurant energy was lost to tank wall heating.

• Geomechanics and Engineering
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• v.31 no.5
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• pp.453-460
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• 2022

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R² value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.62-68
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• 2011

Power system needs to sustain high reliability due to its complexity and security. The reliability prediction method is usually based on independent failure. However, in practice, the Common Cause Failures(CCF) and Cascading failure occur to the facilities in power system as well as independent failures in many cases. The CCF and Cascading failure turn out the system collapse seriously in a wide range. Therefore to improve the reliability of the power system practically, it is required that the analysis is conducted by using the CCF and Cascading failure. This paper describes the CCF and Cascading failure modeling combined with independent failure. The incorporated model of independent failure, CCF and cascading failure is proposed and analyzed, and it is applied to the distribution power system in order to examine this method.

• Earthquakes and Structures
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• v.18 no.2
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• pp.163-170
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• 2020

Floor acceleration plays a major role in the seismic design of nonstructural components and equipment supported by structures. Large floor acceleration may cause structural damage to or even collapse of buildings. For precision instruments in high-tech factories, even small floor accelerations can cause considerable damage in this study. Six P-wave parameters, namely the peak measurement of acceleration, peak measurement of velocity, peak measurement of displacement, effective predominant period, integral of squared velocity, and cumulative absolute velocity, were estimated from the first 3 s of a vertical ground acceleration time history. Subsequently, a new predictive algorithm was developed, which utilizes the aforementioned parameters with the floor height and fundamental period of the structure as the new inputs of a support vector regression model. Representative earthquakes, which were recorded by the Structure Strong Earthquake Monitoring System of the Central Weather Bureau in Taiwan from 1992 to 2016, were used to construct the support vector regression model for predicting the peak floor acceleration (PFA) of each floor. The results indicated that the accuracy of the predicted PFA, which was defined as a PFA within a one-level difference from the measured PFA on Taiwan's seismic intensity scale, was 96.96%. The proposed system can be integrated into the existing earthquake early warning system to provide complete protection to life and the economy.

• 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.