• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.22-27
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• 1990

Since an eigenvalue problem in structural analysis has been recognized as an important process for the assessment of structural strength, it is usually to be carried out the eigenvalue analysis or buckling analysis of structures when the compression behabiour of the member is dorminant. In general, various variables involved in the eigenvalue problem have also shown their variability. So it is natural to apply the probabilistic analysis into such problem. Since the limit state equation for the eigenvalue analysis or buckling reliability analysis is expressed implicitly in terms of random variables involved, the probabilistic finite element method is combined with the conventional reliability method such as MVFOSM and AFOSM for the determination of probability of failure due to buckling. The accuracy of the results obtained by this method is compared with results from the Monte Carlo simulations. Importance sampling method is specially chosen for overcomming the difficulty in a large simulation number needed for appropriate accurate result. From the results of the case study, it is found that the method developed here has shown good performance for the calculation of probability of buckling failure and could be used for checking the safety of the calculation of probability of buckling failure and could be used for checking the safely of frame structure which might be collapsed by either yielding or buckling.

• Nuclear Engineering and Technology
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• v.37 no.3
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• pp.245-258
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• 2005

This paper addresses two types of uncertainty: stochastic uncertainty and subjective uncertainty in probabilistic accident consequence assessments. The off-site consequence assessment code OSCAAR has been applied to uncertainty and sensitivity analyses on the individual risks of early fatality and latent cancer fatality in the population outside the plant boundary due to a severe accident. A new stratified meteorological sampling scheme was successfully implemented into the trajectory model for atmospheric dispersion and the statistical variability of the probability distributions of the consequence was examined. A total of 65 uncertain input parameters was considered and 128 runs of OSCAAR with 144 meteorological sequences were performed in the parameter uncertainty analysis. The study provided the range of uncertainty for the expected values of individual risks of early and latent cancer fatality close to the site. In the sensitivity analyses, the correlation/regression measures were useful for identifying those input parameters whose uncertainty makes an important contribution to the overall uncertainty for the consequence. This could provide valuable insights into areas for further research aiming at reducing the uncertainties.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.204-212
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• 2020

For general target tracking works by receiving a set of measurements from sensor. However, if the SNR(Signal to Noise Ratio) is low due to small RCS(Radar Cross Section), caused by remote small targets, the target's information can be lost during signal processing. TBD(Track Before Detect) is an algorithm that performs target tracking without threshold for detection. That is, all sensor data is sent to the tracking system, which prevents the loss of the target's information by thresholding the signal intensity. On the other hand, using all sensor data inevitably leads to computational problems that can severely limit the application. In this paper, we propose an iterative Joint Integrated Probabilistic Data Association as a practical target tracking technique suitable for a low SNR multi-target environment with real time operation capability, and verify its performance through simulation studies.

• Computers and Concrete
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• v.15 no.4
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• pp.485-501
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• 2015

Cooling by the flow of water through an embedded cooling pipe has become a common and effective artificial thermal control measure for massive concrete structures. However, an extreme thermal gradient induces significant thermal stress, resulting in thermal cracking. Using a mesoscopic finite-element (FE) mesh, three-phase composites of concrete namely aggregate, mortar matrix and interfacial transition zone (ITZ) are modeled. An equivalent probabilistic model is presented for failure study of concrete by assuming that the material properties conform to the Weibull distribution law. Meanwhile, the correlation coefficient introduced by the statistical method is incorporated into the Weibull distribution formula. Subsequently, a series of numerical analyses are used for investigating the influence of the correlation coefficient on tensile strength and the failure process of concrete based on the equivalent probabilistic model. Finally, as an engineering application, damage and failure behavior of concrete cracks induced by a water-cooling pipe are analyzed in-depth by the presented model. Results show that the random distribution of concrete mechanical parameters and the temperature gradient near water-cooling pipe have a significant influence on the pattern and failure progress of temperature-induced micro-cracking in concrete.

• Computers and Concrete
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• v.13 no.2
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• pp.187-207
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• 2014

This paper mainly discusses the influence of the aggregate properties including grading, shape, content and distribution on the chloride diffusion coefficient, as well as the initiation time of steel corrosion from a probabilistic point of view. Towards this goal, a simulation method of random aggregate structure (RAS) based on elliptical particles and a procedure of finite element analysis (FEA) at meso-scale are firstly developed to perform the analysis. Next, the chloride diffusion coefficient ratio between concrete and cement paste $D_{app}/D_{cp}$ is chosen as the index to represent the effect of aggregates on the chloride diffusion process. Identification of the random distribution of this index demonstrates that it can be viewed as actually having a normal distribution. After that, the effect of aggregates on $D_{app}/D_{cp}$ is comprehensively studied, showing that the appropriate properties of aggregates should be decided by both of the average and the deviation of $D_{app}/D_{cp}$. Finally, a case study is conducted to demonstrate the application of this mesoscopic method in predicting the initiation time of steel corrosion in reinforced concrete (RC) structures. The mesoscopic probabilistic method developed in this paper can not only provide more reliable evidences on the proper grading and shape of aggregates, but also play an important role in the probability-based design method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.261-267
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• 2006

The required storage inventory is a very important decision variable which determines the storage capacity of a container terminal. Generally, the required storage inventory is dependent upon such factors as ship headway, allowable dwell time of containers, loading/unloading time per ship, and so on. Until now, the required storage inventory is estimated under the assumption that the factors are deterministic in several studies. However, this study proposes how to estimate a required storage inventory satisfying the required service level under the assumption that a containership's load size is probabilistic. Numerical experiments, which use a simulation show that the proposed method can estimate more adequately the maximum storage inventory than other methods under a probabilistic environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1712-1720
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• 2017

In South Korea, minimum reserve rate, which is to satisfy reliability standard, has been determined by simulation result using WASP. But, it is still controversial whether the level of minimum reserve rate is adequate. Thus, in this study, various analyses of minimum reserve rate are being conducted. WASP uses the probabilistic simulation technique to evaluate whether reliability standard is satisfied. In this process, forced outage rate and maintenance periods of each generator play important roles. Especially, the long-term plan can be varied depending on how maintenance periods deal with. In order to model maintenance periods in the probabilistic simulation technique, WASP uses derating method. However, broad analyses have to be conducted because there are various ways including derating method to model maintenance periods which result in different results. Therefore, in this paper, 3 different maintenance outage rate modeling methods are applied to arbitrarily modeled system based on the basic plan for long-term electricity supply and demand of South Korea. Results show impact of each modeling method on minimum reserve rate.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.545-553
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• 2016

The failure probabilities of the reactor pressure vessel (RPV) for low temperature over-pressurization (LTOP) and cool-down transients are calculated in this study. For the cool-down transient, a pressure-temperature limit curve is generated in accordance with Section XI, Appendix G of the American Society of Mechanical Engineers (ASME) code, from which safety margin factors are deliberately removed for the probabilistic fracture mechanics analysis. Then, sensitivity analyses are conducted to understand the effects of some input parameters. For the LTOP transient, the failure of the RPV mostly occurs during the period of the abrupt pressure rise. For the cool-down transient, the decrease of the fracture toughness with temperature and time plays a main role in RPV failure at the end of the cool-down process. As expected, the failure probability increases with increasing fluence, Cu and Ni contents, and initial reference temperature-nil ductility transition ($RT_{NDT}$). The effect of warm prestressing on the vessel failure probability for LTOP is not significant because most of the failures happen before the stress intensity factor reaches the peak value while its effect reduces the failure probability by more than one order of magnitude for the cool-down transient.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.498-508
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• 2021

Micro reactors are increasingly being considered for utilization as distributed power sources. Hence, the probabilistic safety assessment (PSA) of a direct supercritical-CO₂-cooled fast reactor, called micro modular reactor (MMR), was performed in this study; this reactor was developed using innovative design concepts. It adopted a modular design and passive safety systems to minimize site constraints. As the MMR is in its conceptual design phase, design weaknesses and valuable safety insights could be identified during PSA. Level 1 internal event PSA was carried out involving literature survey, system characterization, identification of initiating events, transient analyses, development of event trees and fault trees, and quantification. The initiating events and scenarios significantly contributing to core damage frequency (CDF) were determined to identify design weaknesses in MMR. The most significant initiating event category contributing to CDF was the transients with the power conversion system initially available category, owing to its relatively high occurrence frequency. Further, an importance analysis revealed that the safety of MMR can be significantly improved by improving the reliability of reactor trip and passive decay heat removal system operation. The findings presented in this paper are expected to contribute toward future applications of PSA for assessing unconventional nuclear reactors in their conceptual design phases.

• Geomechanics and Engineering
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• v.16 no.6
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• pp.655-667
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• 2018

To evaluate the stability of a rock slope with one pre-exiting vertical crack, this paper performs corresponding probabilistic stability analysis. The existence of cracks is generally ignored in traditional deterministic stability analysis. However, they are widely found in either cohesive soil or rock slopes. The influence of one pre-exiting vertical crack on a rock slope is considered in this study. The safety factor, which is usually adopted to quantity the stability of slopes, is derived through the deterministic computation based on the strength reduction technique. The generalized Hoek-Brown (HB) failure criterion is adopted to characterize the failure of rock masses. Considering high nonlinearity of the limit state function as using nonlinear HB criterion, the multivariate adaptive regression splines (MARS) is used to accurately approximate the implicit limit state function of a rock slope. Then the MARS is integrated with Monte Carlo simulation to implement reliability analysis, and the influences of distribution types, level of uncertainty, and constants on the probability density functions and failure probability are discussed. It is found that distribution types of random variables have little influence on reliability results. The reliability results are affected by a combination of the uncertainty level and the constants. Finally, a reliability-based design figure is provided to evaluate the safety factor of a slope required for a target failure probability.