• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.69-73
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• 2005

The failure of geogrids used for soil reinforcement application can be defined as an excessive creep strain which causes the collapse of slopes and embankments. Accordingly, the lifetime is evaluated as a time to reach the excessive creep strain using two accelerated creep testing methods, time-temperature superposition(TTS) and stepped isothermal methods(SIM). TTS is a well-accepted acceleration method to evaluate creep behavior of polymeric materials, while SIM was developed in the last ten years mainly to shorten testing time and minimize the uncertainty associated with inherent variability of multi-specimen tests. The SIM test is usually performed using single rib of geogrids for temperature steps of $14^{\circ}C$ and a dwell time of 10,000 seconds. However, for multi-ribs of geogrids, the applicability of the SIM has not been well established. In this study, the creep behaviors are evaluated using multi-ribs of polyester geogrids using SIM and TTS creep procedures and the newly designed test equipment. Then the lifetime of geogrids are predicted by analyzing the failure times to reach the excessive creep strains through reliability analysis.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.3
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• pp.435-451
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• 2015

Evaluation of the performance of aging structures is essential in the oil and gas industry, where the inaccurate prediction of structural performance can have significantly hazardous consequences. The effects of structure failure due to the significant reduction in wall thickness, which determines the burst strength, make it very complicated for pipeline operators to maintain pipeline serviceability. In other words, the serviceability of gas pipelines and elbows needs to be predicted and assessed to ensure that the burst or collapse strength capacities of the structures remain less than the maximum allowable operation pressure. In this study, several positions of the corrosion in a subsea elbow made of API X42 steel were evaluated using both design formulas and numerical analysis. The most hazardous corrosion position of the aging elbow was then determined to assess its serviceability. The results of this study are applicable to the operational and elbow serviceability needs of subsea pipelines and can help predict more accurate replacement or repair times.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.1
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• pp.14-26
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• 2014

This paper provides the prediction of ultimate longitudinal strengths of the hull girders of a very large crude carrier considering probabilistic damage extent due to collision and grounding accidents based on IMO Guidelines (2003). The probabilistic density functions of damage extent are expressed as a function of non-dimensional damage variables. The accumulated probabilistic levels of 10%, 30%, 50%, and 70% are taken into account for the estimation of damage extent. The ultimate strengths have been calculated using the in-house software called Ultimate Moment Analysis of Damaged Ships which is based on the progressive collapse method, with a new convergence criterion of force vector equilibrium. Damage indices are provided for several probable heeling angles from $0^{\circ}$ (sagging) to $180^{\circ}$ (hogging) due to collision- and grounding-induced structural failures and consequent flooding of compartments. This paper proves from the residual strength analyses that the second moment of area of a damage section can be a reliable index for the estimation of the residual ultimate strength. A simple polynomial formula is also proposed based on minimum residual ultimate strengths.

• Geomechanics and Engineering
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• v.28 no.3
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• pp.313-324
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• 2022

Rock properties are important in the design of mines and civil engineering excavations to prevent the imminent failure of slopes and collapse of underground excavations. However, the time, cost, and expertise required to perform experiments to determine those properties are high. Therefore, empirical models have been developed for estimating the mechanical properties of rock that are difficult to determine experimentally from properties that are less difficult to measure. However, the inherent variability in rock properties makes the accurate performance of the empirical models unrealistic and therefore necessitate the use of soft computing models. In this study, Gaussian process regression (GPR), artificial neural network (ANN) and response surface method (RSM) have been proposed to predict the static and dynamic rock properties from the P-wave and rock density. The outcome of the study showed that GPR produced more accurate results than the ANN and RSM models. GPR gave the correlation coefficient of above 99% for all the three properties predicted and RMSE of less than 5. The detailed sensitivity analysis is also conducted using the RSM and the P-wave velocity is found to be the most influencing parameter in the rock mechanical properties predictions. The proposed models can give reasonable predictions of important mechanical properties of sedimentary rock.

• 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 Korean Tunnelling and Underground Space Association
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• v.12 no.5
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• pp.359-368
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• 2010

Unlike in Korea where steel pipe-reinforced multistep grouting is of commonly used methods for tunnel reinforcement, face bolt method is more widely used due to its better workability and lower construction cost in other countries. In this paper, the effects of both methods after tunnel failure were numerically analyzed and verified based on the oversea construction experiences. As a result it is concluded that the face bolt method may be effective to reinforcement especially when there are some fractured zones developed in the face of tunnel.

• Fire Science and Engineering
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• v.31 no.1
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• pp.26-35
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• 2017

Since the Northridge earthquake (1994) and Kobe earthquake (1995), the concept of performance-based design has been actively introduced to design major structures and buildings. Recently, the seismic design code was established for fire protection facilities. Therefore, the important fire protection facilities should be designed and constructed according to the seismic design code. Accordingly, uniform hazard spectra (UHS), with annual exceedance probabilities, corresponding to the performance level, such as operational, immediate occupancy, life safety, and collapse prevention, are required for performance-based design. Using the method of probabilistic seismic hazard analysis (PSHA), the uniform hazard spectra for 5 major cities in Korea with a recurrence period of 500, 1,000, and 2,500 years corresponding to frequencies of (0.5, 1.0, 2.0, 5.0, 10.0)Hz and PGA, were analyzed. The expert panel was comprised of 10 members in seismology and tectonics. The ground motion prediction equations and several seismo tectonic models suggested by 10 expert panel members in seismology and tectonics were used as the input data for uniform hazard spectrum analysis. According to sensitivity analysis, the parameter of spectral ground motion prediction equations has a greater impact on the seismic hazard than seismotectonic models. The resulting uniform hazard spectra showed maximum values of the seismic hazard at a frequency of 10Hz and also showed the shape characteristics, which are similar to previous studies and related technical guides for nuclear facilities.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.416-429
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• 2018

Typhoons and floods are natural disasters that occur frequently, and the damage resulting from these disasters must be in advance predicted to establish appropriate responses. Direct damages such as building collapse, human casualties, and loss of farms and fields have more attention from people than indirect damages such as increase of consumer prices. But indirect damages also need to be considered for living. The agricultural products are typical consumer items affected by typhoons and floods. Sudden, powerful typhoons are mostly accompanied by heavy rains and damage agricultural products; this increases the retail price of such products. This study analyzes the influence of natural disasters on the price of agricultural products by using a deep learning algorithm. We decided rice, onion, green onion, spinach, and zucchini as target agricultural products, and used data on variables that influence the price of agricultural products to create a model that predicts the price of agricultural products. The result shows that the model's accuracy was about 0.069 measured by RMSE, which means that it could explain the changes in agricultural product prices. The accurate prediction on the price of agricultural products can be utilized by the government to respond natural disasters by controling amount of supplying agricultural products.

• 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.

• Journal of the Korean Geotechnical Society
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• v.33 no.12
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• pp.7-20
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• 2017

In this study, effects of grain size distribution on the shear strength and rheological properties are investigated for coarse- and fine-grained soils by using direct shear apparatus. Shear strengths are estimated for fine-grained soils with the maximum particle size of 0.075 mm and coarse-grained soils with the maximum particle size of 0.425 mm and fine contents of 17% prepared at dry and liquid limit states. The direct shear tests are conducted under the relatively slow shear velocity, which corresponds to the reactivated landslide or debris flow after collapse according to the landslide classification. In addition, for the evaluation of rheological properties, residual shear strengths for both fine- and coarsegrained soils prepared under liquid limit states are obtained by multiple reversal shear tests under three shear velocities. From the relationship between residual shear strengths and shear rates, Bingham plastic viscosity and yield stress are estimated. The direct shear tests show that cohesions of fine-grained soil are greater than those of coarse-grained soil at both dry and liquid limit states. However, internal friction angles of fine-grained soil are smaller than those of coarse-grained soil. In case of rheological parameters, the plastic viscosity and yield stress of fine-grained soils are greater than those of coarse-grained soils. This study may be effectively used for the prediction of the reactivated landslide or debris flow after collapse.