• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.2
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• pp.33-44
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• 2008

Most of the civil structure - bridges, offshore structures, plant, etc. - have been designed by the classical approaches which deal with all the design parameters as deterministic variables. However, some more advanced techniques are required to evaluate the inherent randomness and uncertainty of each design variable. In this research, a seismic safety assessment algorithm based on the structural reliability analysis has been formulated and computerized for more reasonable seismic design of turbine-generator foundations. The formulation takes the design parameters of the system and loading properties as random variables. Using the proposed method, various kinds of parametric studies have been performed and probabilistic characteristics of the resulted structural responses have been evaluated. Afterwards, the probabilistic safety of the system has been quantitatively evaluated and finally presented as the reliability indexes and failure probabilities. The proposed procedure is expected to be used as a fundamental tool to improve the existing design techniques of turbine-generator foundations.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• Journal of Soil and Groundwater Environment
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• v.21 no.6
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• pp.67-79
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• 2016

Gaussian process regression (GPR) is proposed as a tool of long-term groundwater quality predictions. The major advantage of GPR is that both prediction and the prediction related uncertainty are provided simultaneously. To demonstrate the applicability of the proposed tool, GPR and a conventional non-parametric trend analysis tool are comparatively applied to synthetic examples. From the application, it has been found that GPR shows better performance compared to the conventional method, especially when the groundwater quality data shows typical non-linear trend. The GPR model is further employed to the long-term groundwater quality predictions based on the data from two domestically operated groundwater monitoring stations. From the applications, it has been shown that the model can make reasonable predictions for the majority of the linear trend cases with a few exceptions of severely non-Gaussian data. Furthermore, for the data shows non-linear trend, GPR with mean of second order equation is successfully applied.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.2
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• pp.80-87
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• 2020

As the Arctic sea ice decreases due to various reasons such as global warming, the demand for ships and offshore structures operating in the Arctic region is steadily increasing. In the case of sea ice, the anisotropy is caused by the uncertainty inside the material. For most of the research, nevertheless, estimating the ice load has been treated deterministically. With regard to this, in this paper, a four-point bending strength analysis of an ice specimen was attempted using a stochastic finite element method. First, spatial distribution of the material properties used in the yield criterion was assumed to be a multivariate Gaussian random field. After that, a direct method, which is a sort of stochastic finite element method, and a sensitivity method using the sensitivity of response for random variables were proposed for calculating the probabilistic distribution of ice specimen strength. A parametric study was conducted with different mean vectors and correlation lengths for each material property used in the above procedure. The calculation time was about ten seconds for the direct method and about three minutes for the sensitivity methods. As the cohesion and correlation length increased, the mean value of the critical load and the standard deviation increased. On the contrary, they decreased as the friction angle increased. Also, in all cases, the direct and sensitivity methods yielded very similar results.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.1
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• pp.28-40
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• 2015

This paper aims to address the intelligent active vibration control problem of a flexible rectangular plate vibration involving parameter variation and external disturbance. An adaptive sliding mode (ASM) MIMO control strategy and smart piezoelectric materials are proposed as a solution, where the controller design can deal with problems of an external disturbance and parametric uncertainty in system. Compared with the current 'classical' control design, the proposed ASM MIMO control strategy design has two advantages. First, unlike existing classical control algorithms, where only low intelligence of the vibration control system is achieved, this paper shows that high intelligent of the vibration control system can be realized by the ASM MIMO control strategy and smart piezoelectric materials. Second, the system performance is improved due to two additional terms obtained in the active vibration control system. Detailed design principle and rigorous stability analysis are provided. Finally, experiments and simulations were used to verify the effectiveness of the proposed strategy using a hardware prototype based on NI instruments, a MATLAB/SIMULINK platform, and smart piezoelectric materials.

• Geomechanics and Engineering
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• v.8 no.2
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• pp.153-171
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• 2015

The observational method in tunnel engineering allows the evaluation in real time of the actual conditions of the ground and to take measures if its behavior deviates considerably from predictions. However, it lacks a consistent and structured methodology to use the monitoring data to adapt the support system in real time. The definition of limit criteria above which adaptation is required are not defined and complex inverse analysis procedures (Rechea et al. 2008, Levasseur et al. 2010, Zentar et al. 2001, Lecampion et al. 2002, Finno and Calvello 2005, Goh 1999, Cui and Pan 2012, Deng et al. 2010, Mathew and Lehane 2013, Sharifzadeh et al. 2012, 2013) may be needed to consistently analyze the problem. In this paper a methodology for the real time adaptation of the support systems during tunneling is presented. In a first step limit criteria for displacements and stresses are proposed. The methodology uses graphics that are constructed during the project stage based on parametric calculations to assist in the process and when these graphics are not available, since it is not possible to predict every possible scenario, inverse analysis calculations are carried out. The methodology is applied to the "Bois de Peu" tunnel which is composed by two tubes with over 500 m long. High uncertainty levels existed concerning the heterogeneity of the soil and consequently in the geomechanical design parameters. The methodology was applied in four sections and the results focus on two of them. It is shown that the methodology has potential to be applied in real cases contributing for a consistent approach of a real time adaptation of the support system and highlight the importance of the existence of good quality and specific monitoring data to improve the inverse analysis procedure.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.281-293
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• 2002

Empiricism has characterized the traditional methods of pile design; in essence, pile design recommendations are based on the accumulated knowledge of pile behaviour based on the construction and subsequent load testing of piles in soil and rock. In this paper, the traditional approaches to design of piles in rock will be briefly reviewed. It will be shown that the unrelated empirical relationships developed fur rock lead to considerable uncertainty in the design of piles. A new method for predicting the shaft resistance of piles socketed into rock, and based on fundamental principles is outlined. It is shown that the shaft resistance predictions of this method agree well with the field test data for rock and hard soil. It is demonstrated by way of a limited parametric study that shaft roughness and socket diameter are critical factors in the performance of piles constructed in these materials. The application of the method to piles socketed into the granites and gneisses of Korea is discussed by way of a case study and by reference to recent direct shear tests on these rocks.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.392-396
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• 2011

The purpose of this study was to develop the basic wood density (Abbreviated BWD) for Pinus densiflora and to evaluate the applicability of bootstrap simulation method. The data sets were divided into two groups based on eco-types in Korea, one from Gangwon type and the other from Jungbu type. The estimated BWDs derived from bootstrap simulation, which is one of the non-parametric statistics, were 0.418 ($g/cm^3$) in the Pinus densiflora in Gangwon while 0.464 ($g/cm^3$) in the Pinus densiflora in Jungbu. To evaluate the bootstrap simulation, the mean BWD, standard error and 95% confidence interval of probability density were estimated. The number of replication were 100, 500, 1,000, and 5,000 times that showed constant 95% confidence interval, while tended to decrease in terms of standard errors. The results of this study could be very useful to apply basic wood density values to calculate reliable carbon stocks for Pinus densiflora in Korea.

• International journal of steel structures
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• v.18 no.5
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• pp.1617-1630
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• 2018

The crucial differences between conventional rail with split-type connectors and continuous welded rails are axial stress in the longitudinal direction and stability, as well as other issues generated under the influence of loading effects. Longitudinal stresses generated in continuously welded rails on railway bridges are strongly influenced by the nonlinear behavior of the supporting system comprising sleepers and ballasts. Thus, the track structure interaction cannot be neglected. The rail-support system mentioned above has properties of non-uniform material distribution and uncertainty of construction quality. The linear elastic hypothesis therefore cannot correctly evaluate the stress distribution within the rails. The aim of this study is to apply the nonlinear finite element method using the nonlinear coupling interface between the track and structural model and to illustrate the welded rail behavior under the loading effect and uncertain factors of the ballast. Numerical results of nonlinear finite analysis with a three-dimensional solid and frame element model are presented for a typical track-bridge system. A composite plate girder, modeled by solid and shell elements, is also analyzed to consider the behavior of the welded rail. The analysis result showed buckling under the independent calculations of load cases, including 'temperature change', 'bending of the supporting structure', and 'braking' of the railway vehicle. A parametric study of the load combination method and the loading sequence is also included in this analysis.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.552-560
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• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.