• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.1989-1995
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• 2004

Finite element analysis(FEA) is the most popular numerical method to simulate plasticity-induced fatigue crack closure and can predict fatigue crack closure behavior. Finite element analysis under plane stress state using 4-node isoparametric elements is performed to investigate the detailed closure behavior of fatigue cracks and the numerical results are compared with experimental results. The mesh of constant size elements on the crack surface can not correctly predict the opening level for fatigue crack as shown in the previous works. The crack opening behavior for the size mesh with a linear change shows almost flat stress level after a crack tip has passed by the monotonic plastic zone. The prediction of crack opening level presents a good agreement with published experimental data regardless of stress ratios, which are using the mesh of the elements that are in proportion to the reversed plastic zone size considering the opening stress intensity factors. Numerical interpolation results of finite element analysis can precisely predict the crack opening level. This method shows a good agreement with the experimental data regardless of the stress ratios and kinds of materials.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.401-413
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• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• Research in Community and Public Health Nursing
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• v.9 no.1
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• pp.117-127
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• 1998

The Theory of Planned Behavior has been shown to yield great explanatory power in health behavior as well as social behavior. This study was conducted to test the Theory of Planned Behavior in the prediction and intention of smoking cessation behavior in university student smokers. We conveniently sampled 204 university student smokers and investigated using questionaries, analyzing the data with the Pearson product-moment correlation, and multiple regression. The results are as follows : 1. There are significant correlations in direct and indirect measures of attitude toward smoking cessation behavior, subjective norm, and perceived behavioral control. 2. Behavior belief is significant in predicting attitudes toward smoking cessation behavior. Normative belief is significant in predicting the subjective norm. Control belief is significant in predicting perceived behavioral control. 3. Attitude toward smoking cessation behavior, subjective norm are significant in predicting intention of smoking cessation behavior. In conclusion, this study demonstrated strong support for the Theory of the Planned Behavior and its use to predict smoking cessation behavior in university students smokers. But, as perceived behavioral control is not significant in predicting smoking cessation behavior, indepth research is needed to evaluate the usefullness of the Theory of Planned Behavior and Reasoned Action Theory.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.604-607
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• 2005

In the electro-discharge machining the machining performance is closely related to the characteristics of discharge which can be identified from electrical behavior in gap between workpiece and electrode. Therefore, the accurate prediction of electrical behavior in electro-discharge machining (EDM) is useful to process control and optimization. However, any simulation model fur prediction of electrical behavior in EDM process has never been reported until now. In this study, a simulation model is developed to analyze the electrical behavior of electro-discharge plasma which significantly influences electrical behavior in EDM process. For the purpose of this the fundamentals of electro-discharge mechanism such as inception, propagation, formation of plasma channel and termination are investigated to accurately predict the cycle of discharge plasma in EDM. As a result, a mathematical model of electro-discharge plasma is constructed with considering the fundamentals of electro-discharge plasma. Consequently, it is demonstrated that the developed model can predict the electrical behavior of plasma such as electron density in various conditions.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.8
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• pp.1536-1544
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• 1992

A relatively simple prediction method is proposed for initially semicircular surface crack growth under axial loading. The method takes into account the difference in surface crack closure behavior at the depth point and at the surface intersection point, and also the relationship of crack closure for surface crack and through-thickness crack. The prediction method provides conservative estimation for fatigue life within factor of two, and the predicted crack geometry variations agree well with the observed results. As a result, the prediction method proposed here is considered to be useful for engineering application.

• Proceedings of the Korean Geotechical Society Conference
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• 1997.09a
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• pp.3-20
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• 1997

• Journal of Korean Society of Transportation
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• v.11 no.2
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• pp.5-26
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• 1993

In order to analyze travel mode choice behavior, behavioral models including logit model, based on revealed preference theory, have been using easily measurable variables such as individual socioeconomic characteristics and physical attributes of travel modes. But some recent attitudinal models of travel choice behavior have implied that the negligence of individual psychological variables and individual choice constraints in travel mode choice might preclude better prediction of individual travel mode choice behavior. In this context, this study was attempted to reconstruct an attitudinal model(AM), especially focused on the decision rules in travel mode choice decision making process, consistent with the conceptual framework relating individual attitude and choice constraints to choice behavior. And to evaluate the strengths of the AM to other comparative models(logit, linear-additive, conjunctive, lexicographic model) in predicting travel mode choice bebavior, an empirical study of the mode choice in work-trip to CBD in Seoul was performed. According to the results the percent of correct prediction(PCP) derived from the AM was higher than those derived from comparative models by at least 7 to 20% in predicting travel mode choice. But each model produced a different prediction accuracy depending on market segmentation by travel modal users, individual socioeconomic characteristics, transportation system characteristics, and satisfaction levels. The finding that different groups divided by a certain criterion employ different decision rules supports the necessity of developing a choice model such as the AM combining compensatory and noncompensatory decision rules, and suggests that a proposed transportation system management plan or policy may have different effects on each group.

• The Journal of Korean Association of Computer Education
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• v.15 no.1
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• pp.23-31
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• 2012

The most adolescents have become themselves perpetrators and victims unknowingly about variety illegal behaviors of the Information Ethics(IE) in the Information-oriented society. To solve the problem, in this paper, we developed the IE Behavior Prediction model through various social psychology theories. And then, we analyzed the relationship between the factors influencing about the adolescents's behavioral intention to perform the Information Ethics by using regression analysis. As a result, 'Attitudes toward the behavior', 'Subjective norms', 'Perceived behavior control', and 'Past behavior' about behavioral intention of the Information Ethics have performed positively. And also they have statically significant. Especially, 'Attitudes toward the behavior' gave the larger effect than the other 3 factors about the Information Ethics Behavior. This result would provide the useful guideline to develop the educational resources, counsel materials, and test items for Information Ethics Behavior.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.90-99
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• 2004

Stone column is one of the soft ground improvement method, which enhances ground conditions through ground water draining, settlement reducing and bearing capacity increasing complexly by using crushed stone instead of sand in general vertical drain methods. In recent, general construction material, sand is in short of supply, because of the unbalance of demand and supply. Also, the bearing capacity improving effect of stone column method is needed in many cases so the bearing capacity estimation is considered as important point. Nevertheless, adequate estimation methods to predict bearing capacity of stone column considering stone column and improving ground behavior reciprocally is not yet prepared. To contribute this situation, bearing capacity behavior of stone column were simulated as numerically on various property cases of crushed stone and surrounded ground. Through the numerical analysis of simulation results, bearing capacity behavior prediction formula was suggested. This formula was verified by comparing the prediction result with in situ test.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.452-461
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• 2008

Previous settlement prediction method based on settlement monitoring such as hyperbolic, monden method were developed under instantaneous loading condition and have restriction to be applied to soft ground under ramp loading condition. In this study, settlement prediction method under ramp loading was developed. New settlement prediction method under ramp loading considers influence factors of consolidation settlement and increase accuracy of settlement prediction using field monitoring data after ramp loading. Large consolidation tests for ideally controlled one dimensional consolidation under ramp loading condition were performed and the settlement behavior was predicted based on the monitoring data. As a result, new prediction method is expected to have great applicability and practicability for the prediction of settlement behavior.