• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.6
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• pp.35-41
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• 1996

The theory of consolidation has been achieved remarkable development in terms of theory such as finite consolidation theory, two dimensional Rendulic consolidation theory. Though those theories are well defined, the analysis is by no means straightforward, because associated properties are very difficult to determine in the laboratory, Therefore Terzaghi's one dimensional consolidation theory and Barron's cylindrical consolidation theory are still widely used in engineering practice. The theoretical shortcomings of those consolidation theories and uncertainties of associated properties make inevitably some discrepancy between theoretical and field settlements. Field settlement measurement by settlement plate is, therefore, widely used to overcome the discrepancy. Ultimate settlement is one of the most important factor of embankment construction on soft soils. Nowadays the ultimate settlement prediction methods using field settlement data are widely accepted as a helpful tool for field settlement analysis of embankment construction on soft soils. Among the various methods of ultimate settlement prediction, hyperbolic method and Asaoka's method are most commonly used because of their simplicity and ability to give a reasonable estimate of consolidation settlement. In this paper, the reliability of hyperbolic method and Asaoka's method has been examined using analytical methods. It is shown that both hyperbolic method and Asaoka's method are significantly affected by the direction of drainage.

• Journal of Mechanical Science and Technology
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• v.15 no.1
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• pp.44-51
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• 2001

One of the recent issues in design of the spot-welded structure such as the automobile body is to develop an economical prediction method of the fatigue design criterion without additional fatigue test. In this paper, as one of basic investigation for developing such methods, fracture mechanical approach was investigated. First, the Model I, Mode II and Mode III, stress intensity factors were analyzed. Second, strain energy density factor (S) synthetically including them was calculated. And finally, in order to decide the systematic fatigue design criterion by using this strain energy density factor, fatigue data of the ΔP-N(sub)f obtained on the various in-plane bending type spot-welded lap joints were systematically re-arranged in the ΔS-N(sub)f relation. And its utility and reliability were verified by the theory of Weibull probability distribution function. The reliability of the proposed fatigue life prediction value at 10(sup)7 cycles by the strain energy density factor was estimated by 85%. Therefore, it is possible to decide the fatigue design criterion of spot-welded lap joint instead of the ΔP-N(sub)f relation.

• Tribology and Lubricants
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• v.25 no.4
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• pp.256-260
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• 2009

Fretting is a kind of wear which effects on reliability and durability. When machine parts are joined joint in parts such as a bolt or a rivet or a pin, fretting phenomenon is occurred by micro relative movement. When fretting occurs in joint parts, there is wear which is the cause of fatigue crack. Recently, although the ways of assessment of fatigue and damage tolerance are established, there is no way to evaluate fatigue crack initiation life by fretting phenomenon. Consequently, the prediction of life and prevention plan caused by fretting are needed to improve reliability. The objective of this paper is to predict fretting wear by using a experimental method and contact analysis considering wear process. For prediction of fretting wear volume, systematic and controlled experiments with a disc-plate contact under gross slip fretting conditions were carried out. A modified Archard equation is used to calculate wear depths from the contact pressure and stroke using wear coefficients obtained from the disc-plate fretting tests.

• Economic and Environmental Geology
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• v.40 no.1 s.182
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• pp.115-128
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• 2007

This study proposed a modified logistic regression model for a probabilistic prediction of debris flow on natural terrain at the granitic rock area. The modified model dose not contain any categorical factors that were used in the previous model and secured higher reliability of prediction than that of the previous one. The modified model is composed of lithology, two factors of geomorphology, and three factors of soil property. Verification result shows that the prediction reliability is more than 86%. Using the modified regression model, the landslide prediction maps were established. In case of Sacheon area, the prediction map showed that the landslide occurrence was not well corresponded with the model since, even though the forest-fred area was distributed on the center of the model, no factors were considered for the landslide predictions. On the other hand, the prediction model was well corresponded with landslide occurrence at Jumunjin-Yeongok area. The prediction model developed in this study has very high availability to employ in other granitic areas.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.2
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• pp.70-76
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• 2014

In industrial situation, electronic and electro-mechanical systems have been using different type of batteries in rapidly increasing numbers. These systems commonly require high reliability for long periods of time. Wider application of battery for low-power design as a prime power source requires us knowledge of failure mechanism and reliability of batteries in terms of load condition, environment condition and other explanatory variables. Battery life is an important factor that affects the reliability of such systems. There is need for us to understand the mechanism leading to the failure state of battery with performance characteristic and develop a method to predict the life of such battery. The purpose of this paper is to develope the methodology of monitoring the health of battery and determining the condition or fate of such systems through the performance reliability to predict the remaining useful life of primary battery with load condition, operating condition, environment change in light of battery life variation. In order to evaluate on-going performance of systems and subsystems adopting primary batteries as energy source, The primitive prototype for performance reliability analysis device was developed and related framework explained.

• Journal of Korean Society for Quality Management
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• v.43 no.3
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• pp.373-382
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• 2015

Purpose: In this paper, we developed a reliability index (RI) to efficiently compare reliability of products based on the design, development and production information such as reliability tests, quality, product life-cycle management. RI also can be applied to reliability prediction of a novel product as well as comparison evaluation among existing products. Methods: For evaluating RI, we proposed evaluation process which is composed of five steps. Target modules are selected based on warranty data and correlation analysis. Scores of selected target modules are calculated by scoring function. Finally, weights of RI model are determined by optimization method. Results: This paper presented an empirical analysis based on failure data of mobile devices. In this case study, we demonstrated that there is a direct correlation between evaluated RI and field failure probability of each product. Conclusion: We proposed the index for comprehensive and effective assessment of product reliability level. From the procedure of this study, we expected to be applied for reliability estimation of novel products and deduction of field failure-related factors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.599-604
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• 2018

For missile systems, sustaining high reliability and ensuring economical maintenance are very important. In the Republic of Korea, for most missiles, periodic inspection is mandatory for missiles in the field. Every fixed number of years, they are returned to the ordnance depot to be tested and repaired if faults are found. Almost all missiles have a built-in test (BIT) capability. With the BIT, faulty missiles can be isolated anytime during operations or storage in the launchers. So the reliability and the maintenance costs of the missiles greatly depend on the length of the inspection cycle and the BIT/inspection quality. In this paper, we suggest a model for predicting the storage reliability of missiles subjected to non-periodic tests and periodic inspections, excluding overlapping failures. Some numerical examples are given. This model will be useful for determining the length of the periodic inspection cycle.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.349-358
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• 2008

In performing the reliability analysis for predicting the settlement with time of alluvial clay layer at Kobe airport, the uncertainties of geotechnical properties were examined based on the stochastic and probabilistic theory. By using Terzaghi's consolidation theory as the objective function, the failure probability was normalized based on AFOSM method. As the result of reliability analysis, the occurrence probabilities for the cases of the target settlement of ${\pm}10%,\;{\pm}25%$ of the total settlement from the deterministic analysis were 30~50%, 60%~90%, respectively. Considering that the variation coefficients of input variable are almost similar as those of past researches, the acceptable error range of the total settlement would be expected in the range of 10% of the predicted total settlement. As the result of sensitivity analysis, the factors which affect significantly on the settlement analysis were the uncertainties of the compression coefficient Cc, the pre-consolidation stress Pc, and the prediction model employed. Accordingly, it is very important for the reliable prediction with high reliability to obtain reliable soil properties such as Cc and Pc by performing laboratory tests in which the in-situ stress and strain conditions are properly simulated.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.