• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.2 s.25
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• pp.53-63
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• 2007

This study is to evaluate the probable snowfall depth by the point frequency analysis and to draw the map of probable snowfall depth in Korea. The 14 probability distributions which has been widely used in hydrologic frequency analysis are applied to the annual maximum depth of snowfall data. The parameters of each probability distribution are estimated by method of moments, maximum likelihood method and method of probability weighted moments. The estimated parameters were checked by parameter validity conditions of each assumed probability distribution. Four tests that are $X^2-test$, Kolmogorov-Smirnov test, Cramer von Mises test and probability plot correlation coefficient test are used in this study to determine the goodness of fit of the distributions. Mostly the 2-parameter gamma distribution was determined as appropriate distribution for the annual maximum new snowfall depth. The probable snowfall depth were obtained from appropriate distribution for the selected return periods and the maps of probable snowfall depth were presented. It will be useful to specify the snowfall load for the design of agricultural facilities such as vinyl house and cattle shed.

• Nuclear Engineering and Technology
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• v.29 no.6
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• pp.444-450
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• 1997

The current ASME code for reinforced concrete containment structures are not based on probability concepts. The stochastic nature of natural hazard or accidental loads and the variations of material properties require a probabilistic approach for a rational assessment of structural safety and performance. The paper develops design load factors for the serviceability limit state of reinforced concrete containment structures. The target limit state probability is determined and the load factors are calculated by the numerical analysis. Design load factors are proposed and carried out the reliability assessments.

• International Journal of Reliability and Applications
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• v.14 no.2
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• pp.107-114
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• 2013

Modelling of failures is an important element of reliability modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.373-379
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• 2017

Seismic probabilistic safety assessments are used to help understand the impact potential seismic events can have on the operation of a nuclear power plant. An important component to seismic probabilistic safety assessment is the seismic hazard curve which shows the frequency of seismic events. However, these hazard curves are estimated assuming a normal distribution of the seismic events. This may not be a strong assumption given the number of recorded events at each source-to-site distance. The use of a normal distribution makes the calculations significantly easier but may underestimate or overestimate the more rare events, which is of concern to nuclear power plants. This paper shows a preliminary exploration into the effect of using a distribution that perhaps more represents the distribution of events, such as the t-distribution to describe data. The integration of a probability distribution with potentially larger tails basically pushes the hazard curves outward, suggesting a different range of frequencies for use in seismic probabilistic safety assessments. Therefore the use of a more realistic distribution results in an increase in the frequency calculations suggesting rare events are less rare than thought in terms of seismic probabilistic safety assessment. However, the opposite was observed with the ground motion prediction equation considered.

• Earthquakes and Structures
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• v.21 no.5
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• pp.461-475
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• 2021

Strategic structures are a potential target of the growing terrorist attacks, so their performance under explosion hazard has been paid attention by researchers in the last years. In this regard, the aim of this study is to evaluate the blast-resistance performance of lead-rubber bearing (LRB) base isolation system based on a probabilistic framework while uncertainties related to the charge weight and standoff distance have been taken into account. A sensitivity analysis is first performed to show the effect of explosion uncertainty on the response of base-isolated buildings. The blast fragility curve is then developed for three base-isolated steel moment-resisting buildings with different heights of 4, 8 and 12 stories. The results of sensitivity analysis show that although LRB has the capability of reducing the peak response of buildings under explosion hazard, this control system may lead to increase in the peak response of buildings under some explosion scenarios. This shows the high importance of probabilistic-based assessment of isolated structures under explosion hazard. The blast fragility analysis shows effective performance of LRB in mitigating the probability of failure of buildings. Therefore, LRB can be introduced as effective control system for the protection of buildings from explosion hazard regarding uncertainty effect.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.455-466
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• 2013

In nested case-control studies, the most common way to make inference under a proportional hazards model is the conditional logistic approach of Thomas (1977). Inclusion probability methods are more efficient than the conditional logistic approach of Thomas; however, the epidemiology research community has not accepted the methods as a replacement of the Thomas' method. This paper promotes the inverse probability weighting method originally proposed by Samuelsen (1997) in combination with an approximate jackknife standard error that can be easily computed using existing software. Simulation studies demonstrate that this approach yields valid type 1 errors and greater powers than the conditional logistic approach in nested case-control designs across various sample sizes and magnitudes of the hazard ratios. A generalization of the method is also made to incorporate additional matching and the stratified Cox model. The proposed method is illustrated with data from a cohort of children with Wilm's tumor to study the association between histological signatures and relapses.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.57-70
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• 2012

The aim of this study is to analyse the landslide susceptibility and the future hazard in Inje, Korea using probability rainfalls and artificial neural network (ANN) environment based on geographic information system (GIS). Data for rainfall probability, topography, and geology were collected, processed, and compiled in a spatial database using GIS. Deokjeok-ri that had experienced 694 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 1-day rainfall of 202 mm or 3-day cumulative rainfalls of 449 mm.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.622-625
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• 2004

This paper presents multi-source spatial data integration models based on probability theory for landslide hazard assessment. Four probabilistic models such as empirical likelihood ratio estimation, logistic regression, generalized additive and predictive discriminant models are proposed and applied. The models proposed here are theoretically based on statistical relationships between landslide occurrences and input spatial data sets. Those models especially have the advantage of direct use of continuous data without any information loss. A case study from the Gangneung area, Korea was carried out to quantitatively assess those four models and to discuss operational issues.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.13-24
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• 2009

This paper introduces various kinds of applications of the scenario-based seismic risk assessment in Taiwan. Seismic scenario simulation (SSS) is a GIS-based technique to assess distribution of ground shaking intensity, soil liquefaction probability, building damages and associated casualties, interruption of lifeline systems, economic losses, etc. given source parameters of an earthquake. The SSS may integrate with rapid earthquake information release system to obtain valuable information and to assist in decision-making processes to dispatch rescue and medical resources efficiently. The SSS may also integrate with probabilistic seismic hazard analysis to evaluate various kinds of risk estimates, such as average annual loss and probable maximum loss in one event, in a probabilistic sense and to help proposing feasible countermeasures.

• Computational Structural Engineering
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• v.4 no.3
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• pp.107-115
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• 1991

A probabilistic-Fuzzy model for seismic hazard analysis is developed. The proposed model is able to reproduce both the randomness and the imprecision in conjunction with earthquake occurrences. Results-of this research are (a) membership functions of both peak ground accelerations associated with a given probability of exceedance and probabilities of exceedance associated with a given peak ground acceleration, and (b) characteristic values of membership functions at each location of interest. The proposed probabilistic-fuzzy model for assessment of seismic hazard is successfully applied to the Wasatch Front Range in Utah in order to obtain the seismic maps for different annual probabilities of exceedance, different peak ground accelerations, and different time periods.