• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.6
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• pp.589-597
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• 2015

In this paper, the live load model for the design of high-speed railway bridge is analyzed by statistic and probabilistic methods and the safety level that is given by the load factors of the load combination is analyzed. This study is a part of the development of the limit state design method for the railway bridge, and the train data collected from the Gyeongbu high-speed railway for about one month are utilized. The four different statistical methods are applied to estimate the design load to match the bridge design life and the results are compared. In order to examine the safety level that the design load combination of the railway bridge gives, the reliability indexes are determined and the results are analyzed. The load effect from the current design live load for the high-speed rail bridge which is 0.75 times of the standard train load is came out greater than at least 30-22% that from the estimated load from the measured data. If it is judged based on the ultimate limit state, there is a possibility of additional reduction of the safety factors through the reliability analysis.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.13 no.1
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• pp.1-10
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• 2015

In the safety assessment of the geological disposal system of the radioactive wastes, the abnormal scenarios, in which the system is impacted by the abnormal events, need to be considered in addition to the reference scenario. In this study, characterization and prediction of well intrusion as one of the abnormal events which will impact the disposal system were conducted probabilistically and statistically for the safety assessment. The domestic well development data were analyzed, and the prediction methodologies of the well intrusion were suggested with a computation example. From the results, the annual well development rate per unit area in Korea was about 0.8 well/yr/km² in the conservative point of view. Considering the area of the overall disposal system which is about 1.5 km², the annual well development rate within the disposal system could be 1.2 well/yr. That is, it could be expected that more than one well would be installed within the disposal system every year after the institutional management period. From the statistical analysis, the probabilistic distribution of the well depth followed the log-normal distribution with 3.0363 m of mean value and 1.1467 m of standard deviation. This study will be followed by the study about the impacts of the well intrusion on the geological disposal system, and the both studies will contribute to the increased reliability of safety assessment.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.4
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• pp.349-360
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• 2008

In this paper, the modeling procedure of random field with an elasto-plastic finite element algorithm and probability of failure on closely-spaced tunnel were investigated. Local average subdivision (LAS) method which can generate discrete random variables fast and accurately as well as change the resolution in certain region was used. And correlated value allocating and weighted average method were suggested to implement geometrical characteristics of tunnel. After the probability of failure on the test problem was thoroughly investigated using random finite element method, the results were compared with the deterministic strength reduction factor method and single random variable method. Of particular importance in this work, is the conclusion that the probability of failure determined by simplified probabilistic analysis, in which spatial variability is ignored by assuming perfect correlation, can be estimated from the safety factor determined by strength reduction factor method. Also, single random variable method can lead to unconservative estimates of the probability of failure.

• Journal of the Korean Society for Railway
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• v.12 no.5
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• pp.744-750
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• 2009

According to the result of related studies, the degradation of human performance has been revealed as one of the most significant causes resulting in the safety of any human-involved system. This means that preventing the occurrence of accidents/incidents through avoiding the degradation of human performance is prerequisite for their successive operation. To this end, it is necessary to develop a plausible tool to evaluate the complexity of a task, which has been known as one of the decisive factors affecting the human performance. For this reason, in this paper, the complexity of tasks to be conducted by KTX drivers was quantified by TACOM measure that is enable to quantify the complexity of proceduralized tasks being used in nuclear power plants. After that, TACOM scores about the tasks of KTX drivers were compared with NASA-TLX scores that are responsible for the level of a subjective workload to be felt by KTX drivers.

• Nuclear Engineering and Technology
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• v.39 no.3
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• pp.215-220
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• 2007

The existing flooding Probabilistic Safety Analysis(PSA) was updated to reflect the Korean plant specific operating experience data into the flooding frequency to improve the PSA quality. Both the Nuclear Power Experience(NPE) database and the Korea Nuclear Pipe Failure Database(NuPIPE) databases were used in this study, and from these databases, only the Pressurized Water Reactor(PWR) data were used for the flooding frequencies of the flooding areas in the primary auxiliary building. With these databases and a Bayesian method, the flooding frequencies for the flooding areas were estimated. Subsequently, the Core Damage Frequency(CDF) for the flooding PSA of the Ulchin(UCN) unit 3 and 4 plants based on the Korean Standard Nuclear Power Plant(KSNP) internal full-power PSA model was recalculated. The evaluation results showed that sixteen flooding events are potentially significant according to the screening criterion, while there were two flooding events exceeding the screening criterion of the existing UCN 3 and 4 flooding PSA. The result was compared with two kinds of cases: (1) the flooding frequency and CDF from the method of the existing flooding PSA with the PWR and Boiled Water Reactor(BWR) data of the NPE database and the Maximum Likelihood Estimate(MLE) method and (2) the flooding frequency and CDF with the NPE database(PWR and BWR data), NuPIPE database, and a Bayesian method. From the comparison, a difference in CDF results was revealed more clearly between the CDF from this study and case (2) than between case (1) and case (2). That is, the number of flooding events exceeding the screen criterion further increased when only the PWR data were used for the primary auxiliary building than when the Korean specific data were used.

• Steel and Composite Structures
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• v.41 no.6
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• pp.831-850
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• 2021

Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winklersoil model, analytical equations for the moment-rotation response ofsoil during mining induced ground movements are developed. To define the full static moment-rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment-rotation curve. The maximal moment-rotation response of the footing is defined by X = 0:6. Despite the use of Winkler model, the computed moment-rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.315-325
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• 2020

This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.

• Journal of Radiation Protection and Research
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• v.40 no.4
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• pp.244-251
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• 2015

The containment filtered venting system reduces the range of the contamination area around the nuclear power plant by strengthening the integrity of the containment building. In this study, the probabilistic assessment code MACCS2 was used to assess the effect of the CFVS to off-site. The accident source term was selected from a Probabilistic Safety Analysis report of SHINKORI 1&2 Nuclear Power Plant. The three source term categories from 19 STC were chosen to evaluate the effective dose and thyroid dose of residents around the power plant and the dose with CFVS and without CFVS were compared. The dose was calculated according to the distance from the nuclear power plant, so the damage scale based on the distance that exceeds the IAEA criteria for effective dose (100 mSv per 7 days) and thyroid dose (50 mSv per 7 days) were compared. The effective dose reduction rates of the STC-3, STC-4, STC-6 were about 95-99% in the whole range (0~35 km), 96-98% for the thyroid dose. There are similar results between effective dose and thyroid dose. After applying the CFVS, the damage scale that exceeds the effective dose criteria was about 1 km (mean). Especially, the STC-4 damage scale was decreased from 26 km (mean) to 1.2 km (mean) significantly. The damage scale that exceed the thyroid dose criteria was decreased to 2~3 km (mean). The STC-4 damage scale was also decreased significantly as compared to STC-3, STC-6 in terms of effective dose.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.853-865
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• 2014

Hydrologic dam risk analysis depends on complex hydrologic analyses in that probabilistic relationship need to be established to quantify various uncertainties associated modeling process and inputs. However, the systematic approaches to uncertainty analysis for hydrologic risk analysis have not been addressed yet. In this paper, two major innovations are introduced to address this situation. The first is the use of a Hierarchical Bayesian model based regional frequency analysis to better convey uncertainties associated with the parameters of probability density function to the dam risk analysis. The second is the use of Bayesian model coupled HEC-1 rainfall-runoff model to estimate posterior distributions of the model parameters. A reservoir routing analysis with the existing operation rule was performed to convert the inflow scenarios into water surface level scenarios. Performance functions for dam risk model was finally employed to estimate hydrologic dam risk analysis. An application to the Dam in South Korea illustrates how the proposed approach can lead to potentially reliable estimates of dam safety, and an assessment of their sensitivity to the initial water surface level.