• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.93-101
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• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• Journal of the Korea Concrete Institute
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• v.21 no.1
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• pp.65-74
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• 2009

Glass fiber reinforced polymer (GFRP) bars are sometimes used when corrosion of conventional reinforcing steel bar is of concern. In this study, a total of 36 beams and one-way slabs reinforced using GFRP bars were tested in flexure. Four different GFRP bars of 13 mm diameter were used in the test program. In most test specimens, the GFRP bars were lap spliced at center. All beams and slabs were tested under 4-point loads so that the spliced region be subject to constant moment. Test variables were splice lengths, cover thicknesses, and bar spacings. No stirrups were used in the spliced region so that the tests result in conservative bond strengths. Average bond stresses that develop between GFRP bars and concrete were determined through nonlinear analysis of the cross-sections. An average bond stress prediction equation was derived utilizing two-variable linear regression. A splice length equation based on 5% fractile concept was then developed. As a result of this study, a rational equation with which design splice lengths of the GFRP bars can be determined, was proposed.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.

• Steel and Composite Structures
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• v.16 no.4
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• pp.437-454
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• 2014

The influence of spacers on the behaviour and ultimate capacity of intermediate length CFS open section columns under axial compression is investigated in this paper. The focus of the research lies in the cross- section predominantly, failed by distortional buckling. This paper made an attempt to either delay or eliminate the distortional buckling mode by the introduction of transverse elements referred herein as spacers. The cross-sections investigated have been selected by performing the elastic buckling analysis using CUFSM software. The test program considered three different columns having slenderness ratios of 35, 50 & 60. The test program consisted of 14 pure axial compression tests under hinged-hinged end condition. Models have been analysed using finite element simulations and the obtained results are compared with the experimental tests. The finite element package ABAQUS has been used to carry out non-linear analyses of the columns. The finite element model incorporates material, geometric non-linearities and initial geometric imperfection of the specimens. The work involves a wide parametric study in the column with spacers of varying depth and number of spacers. The results obtained from the study shows that the depth and number of spacers have significant influence on the behaviour and strength of the columns. Based on the nonlinear regression analysis the design equation is proposed for the selected section.

• Journal of Ocean Engineering and Technology
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• v.30 no.1
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• pp.25-31
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• 2016

In this study, hydrodynamic coefficients were obtained from a Rotating Arm (RA) test, which is one of the captive model tests used to provide accurate coefficients in the control motion equation of an underwater vehicle. The RA test was carried out at the RA facility of ADD (Agency for Defense Development), and the forces and moments acting on the underwater vehicle were measured using a six-axis waterproof gage. A multiple regression analysis was used in the analysis of the measured data. The experimental results were also verified by comparison with the theoretical values of the previous linear coefficients. In addition, the stability indices in the horizontal plane were calculated using the linear and nonlinear coefficients, and the dynamic stability of the underwater vehicle was estimated to have a good dynamic performance with a depth ratio of 6.0.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.