• Journal of the Korean Geotechnical Society
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• v.23 no.9
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• pp.17-28
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• 2007

Urban tunnels are usually important in terms of prediction and control of surface settlement, gradient and ground displacement. This paper has studied the application of strain softening analysis to predict deformation behavior of an urban NATM tunnel. The applied strain softening model considered the reduction of shear stiffness and strength parameter after yielding with strain softening effects of a given material. Measurements of surface subsidence and ground displacement were adopted to monitor the ground behavior resulting from the tunneling and to modify tunnel design. The numerical analysis results produced a strain distribution, deformational mechanism and surface settlement profile, which are in good agreement with the results of case study. The approach of strain softening modeling is expected to be a good prediction method on the ground displacement associated with NATM tunneling at shallow depth and soft ground.

• Journal of Korean Academy of Nursing
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• v.27 no.4
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• pp.796-806
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• 1997

The majority of studies on breastfeeding consists of descriptive correlational studies identifying the incidence and correlates of breastfeeding. The theory of planned behavior has been shown to yield great predictive power for behavioral goals over which individuals have only limited control such as improving school grades and weight loss. The purpose of this study was to test the "theory of planned behavior" in the prediction of breastfeeding of mothers who delivered vaginally, One hundred mothers who delivered vaginally in one general hospital in Seoul and one general hospital and three private hospitals in Taejeon participated in this study. The instruments used for data collection in this study were developed by the researchers following the guidelines suggested by Ajzen & Fishbein(1980) and Ajzen & Madden(1986). The instruments included measurement of attitude, subjective norm, perceived behavioral control and intention. The collected data were analyzed using descriptive statistics, Pearson product moment correlation, hierachical multiple regression and logistic regression. The results are as follows ; 1. Intention to breastfeed correlated significantly with attitude, subjective norm and perceived behavioral control. Both attitude and subjective norm did not make a significant contribution to the prediction of intention, but the addition of perceived behavioral control to the regression equation greatly improved the model's predictive power, increasing the R²from .05 to .52. 2. Intention to breastfeed alone had a significant predictive effect on actual breastfeeding, resulting in a regression coefficient of .16(X²=8 60, p<.01), but when perceived behavioral control was added to the equation, intention was not a significant predictive variable and only perceived behavioral control showed significant predictive power on actual breastfeeding, resulting in a regression coefficient of .12(X²=4.69, p<.05). In sum, breastfeeding behavior lent only partial support to the second version of the theory of planned behavior, and because perceived behavioral control had a strong effect on intention to breastfeed and actual breastfeeding, It would be desirable to develop nursing intervention programs which focus on strengthening the perceived behavioral control for the promotion of breastfeeding.

• Structural Engineering and Mechanics
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• v.77 no.2
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• pp.167-177
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• 2021

Due to various benefits such as unlimited degrees of freedom, environment adaptability, and safety for humans, engineers have used soft materials with hyperelastic behavior in various industrial, medical, rescue, and other sectors. One of the applications of these materials in the fabrication of bending soft actuators (SA) is that they have eliminated many problems in the actuators such as production cost, mechanical complexity, and design algorithm. However, SA has complexities, such as predicting and monitoring behavior despite the many benefits. The first part of this paper deals with the prediction of SA behavior through mathematical models such as Ogden and Darijani, and its comparison with the results of experiments. At first, by examining different geometric models, the cubic structure was selected as the optimal structure in the investigated models. This geometrical structure at the same pressure showed the most significant bending in the simulation. The simulation results were then compared with experimental, and the final gripper model was designed and manufactured using a 3D printer with silicone rubber as for the polymer part. This geometrical structure is capable of bending up to a 90-degree angle at 70 kPa in less than 2 seconds. The second section is dedicated to monitoring the bending behavior created by the strain sensors with different sensitivity and stretchability. In the fabrication of the sensors, silicon is used as a soft material with hyperelastic behavior and carbon fiber as a conductive material in the soft material substrate. The SA designed in this paper is capable of deforming up to 1000 cycles without changing its characteristics and capable of moving objects weigh up to 1200 g. This SA has the capability of being used in soft robots and artificial hand making for high-speed objects harvesting.

• Journal of the Korean GEO-environmental Society
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• v.2 no.3
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• pp.81-88
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• 2001

The behavior of porous medium is modeled by linear thermoporoelastic behavior, linear poroviscoelastic behavior, poroplastic behavior, and poroviscoplastic behavior, etc. The behavior has, in general, a complicated aspect which makes a mechanical description of the problem with time. Constitutive modeling for deformation behavior of porous medium with coupling effects is needed since there is interaction between the constituents in pores with a relative velocity to each other. In this work, it is explained 3-dimensional behavior depending on degree of saturation for porous medium composed of homogeneous, isotropic materials. It is obtained the governing equations based on continuum porous mechanics. In addition, it is developed constitutive model which can be understood of behavior for porous medium which can be understood, analysed behavior of porous medium. It can be accomplished exact analysis and prediction of behavior in porous medium. The behavior for porous medium is analysed exactly, and the prediction of deformation behavior is accomplished. Consequently, it will be basis to analyze 3-dimensional behavior in municipal solid waste landfill, and the practical using of porous medium ground which are composed of nonhomogeneous, anisotropic materials can be done widely.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.3
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• pp.63-82
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• 2014

The main objective of this research is to investigate whether the RFM (recency-frequency-monetary value) information of a customer's redemption behavior of loyalty points can improve the prediction of future value of the customer. The conventional measurement of customer value has been primarily based on purchase transactions behavior although a customer's future behavior can be also influenced by other interactions between the customer and the firm such as redemption of rewards in a loyalty program. We theorize why a customer's redemption behavior can influence her future purchases and thereby the customer's total value based on operant learning theory, goal gradient hypothesis, and lock-in effect. Using a dataset from a major book store in Korea spanning three years between 2008 and 2010, we analyze both purchase transactions and redemption records of over 10,000 customers. The results show that the redemption-based RFM information does improve the prediction accuracy of the customer's future purchases. Based on this result, we also propose an improved estimate of customer lifetime value (CLV) by combining purchase transactions and loyalty points redemption data. Managerial implications will be also discussed for firms managing loyalty programs to maximize the total value customers.

• Elastomers and Composites
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• v.51 no.4
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• pp.308-316
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• 2016

The rubber materials are widely used in automobile industry due to their capability of a large amount of elastic deformation under a force. Current trend of design process requires prediction of functional properties of parts at early stage. The behavior of rubber material can be modeled using strain energy density function. In this study, five different strain energy density functions - Neo-Hookean model, Reduced Polynomial $2^{nd}$ model, Ogden $3^{rd}$ model, Arruda Boyce model and Van der Waals model - were used to estimate the behavior of carbon black added natural rubber under uniaxial load. Two kinds of tests - uniaxial tension test and biaxial tension test - were performed and used to correlate the coefficients of the strain energy density function. Numerical simulations were carried out using finite element analysis and compared with experimental results. Simulation revealed that Ogden $3^{rd}$ model predicted the behavior of carbon added natural rubber under uniaxial load regardless of experimental data selection for coefficient correlation. However, Reduced Polynomial $2^{nd}$, Ogden $3^{rd}$, and Van der Waals with uniaxial tension test and biaxial tension test data selected for coefficient correlation showed close estimation of behavior of biaxial tension test. Reduced Polynomial $2^{nd}$ model predicted the behavior of biaxial tension test most closely.

• The Journal of Korean Association of Computer Education
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• v.21 no.3
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• pp.51-58
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• 2018

In order to analyze the prediction of the computer entertainment behavior, this study investigated the variables' interdependencies and their causal relations to the computer entertainment behavior using Bayesian inference with the Korean Children and Youth Panel Survey data. For the study, Markov blanket was extracted through General Bayesian Network and the degree of influences was investigated by changing the variables' probabilities. Results showed that the computer entertainment behavior was significantly changed depending on adjusting the values of the related variables; school learning act, smoking, taunting, fandom, and school rule. The results suggested that the Bayesian inference could be used in educational filed for predicting and adjusting the adolescents' computer entertainment behavior.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1347-1356
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• 2024

Measuring the residual stress in the components in nuclear power plants is crucial to their safety evaluation. The instrumented indentation technique is a minimally invasive approach that can be conveniently used to determine the residual stress in structural materials in service. Because the indentation behavior of a structure with residual stresses is closely related to the elastic-plastic behavior of the indented material, an accurate understanding of the elastic-plastic behavior of the material is essential for evaluation of the residual stresses in the structures. However, due to the analytical problems associated with solving the elastic-plastic behavior, empirical equations with limited applicability have been used. In the present study, the impact of the non-equibiaxial residual stress state on indentation behavior was investigated using finite element analysis. In addition, a new nonequibiaxial residual-stress prediction methodology is proposed using a convolutional neural network, and the performance was validated. A more accurate residual-stress measurement will be possible by applying the proposed residual-stress prediction methodology in the future.

• Transactions of Materials Processing
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• v.7 no.3
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• pp.197-206
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• 1998

This paper presents the prediction of dynamic recrystallization behavior during hot forging of Inconel 718. Another experiment of pancake forging was also carried out to examine the recrystallization ration dynamically recrystallizaed grain size, and grain growth in the forging. In experiments cylindrical billets were forged by two operations with variations of forging temperature, reduction ration of deformation. and preheating process at each forging step. Also the finite element program, developed here for the prediction using the metallurgical models was used for the analysis of to Inconel 718 upsetting and the results were compared with experimental ones.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1324-1334
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• 2005

This study investigated the applicability of the Artificial Neural Network (ANN) technique for prediction of tunnel behavior. For training data collection, a series of finite element analyses were conducted for actual tunnel project site. Using the data, optimimzed ANNs were developed through a sensitivity study on internal parameters. The developed ANNs can make tunneling related predictions such as tunnel crown settlement, shotcrete lining stress, ground surface settlement, and groundwater inflow rate. The results indicated that the developed ANNs can be used as an effective and efficient tool for tunnelling related prediction in practical tunneling situations.