• Journal of the Korean Society of Safety
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• v.39 no.3
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• pp.75-83
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• 2024

Accurately assessing and managing risks in any endeavor is crucial. Risk assessment in engineering translates the abstract concept of risk into actionable strategies for systematic risk management. However, risk validation is met with significant skepticism, particularly concerning the uncertainty of probability. This study aims to address the aforementioned uncertainty in a multitude of ways. Firstly, instead of relying on deterministic probability, it acknowledges uncertainty and presents a probabilistic interval. Secondly, considering the uncertainty interval highlighted in OREDA, it delineates the bounds of the probabilistic interval. Lastly, it investigates how much explanatory power deterministic probability has within the defined probabilistic interval. By utilizing fault tree analysis (FTA) and integrating confidence intervals, a probabilistic risk assessment was conducted to scrutinize the explanatory power of deterministic probability. In this context, explanatory power signifies the proportion of probability within the probabilistic risk assessment interval that lies below the deterministic probability. Research results reveal that at a 90% confidence interval, the explanatory power of deterministic probability decreases to 73%. Additionally, it was confirmed that explanatory power reached 100% only with a probability application 36.9 times higher.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.435-443
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• 2010

When testing systems that incorporate probabilistic behavior, it is necessary to apply test inputs a number of times in order to give a test verdict. Interval estimation can be used to assert the correctness of probabilities where the selection of confidence interval is one of the important issues for quality of testing. The Wald interval has been widely accepted for interval estimation. In this paper, we compare the Wald interval and the Agresti-Coull interval for various sizes of samples. The comparison is carried out based on the test pass probability of correct implementations and the test fail probability of incorrect implementations when these confidence intervals are used for probability testing. We consider two-sided confidence intervals to check if the probability is close to a given value. Also one-sided confidence intervals are considered in the comparison in order to check if the probability is not less than a given value. When testing probabilities using two-sided confidence intervals, we recommend the Agresti-Coull interval. For one-sided confidence intervals, the Agresti-Coull interval is recommended when the size of samples is large while either one of two confidence intervals can be used for small size samples.

• Journal of Korea Port Economic Association
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• v.25 no.4
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• pp.165-182
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• 2009

This study analyzed the economic analysis on the Cambodian inland waterway from Phnom Penh to Chong Kneas. The social discount rate of 3.5% was applied for the cost and benefit of projects and converted to the current values in 2009. The benefits were supposed as the triangle distribution with minimum, mode, and maximum value corresponding to pessimistic, moderate and optimistic prospect separately. And the distributions of costs were the normal. As the result of probabilistic simulations, the average of B/C for scenario A showed relatively the highest with 0.25 and its 90% confidence interval 0.16~0.35. The average B/C of scenario B is 0.10 with the 90% confidence interval 0.06~0.13 and the one of scenario C is 0.15 with 90% confidence interval 0.12~0.19. Therefore it was concluded as low economic feasibility to install inland waterway aids to navigation along the surveyed waterway. However, the performance of the project should be determined by its political analysis as well as the economic.

• The Korean Journal of Applied Statistics
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• v.37 no.6
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• pp.801-815
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• 2024

Future uncertainty means that future results or phenomena cannot be accurately predicted. Since deterministic population projection based on such future uncertainty has clear limitations, so many advanced population research institutes and international organizations have emphasized the importance of probabilistic population prediction. It also presents probabilistic predictions in the research areas of climate, process, precipitation, and weather. However, the KOSTAT and various organizations in korea are only in scenario-based deterministic population projection, and only the need for probabilistic population prediction is raised. Therefore, this paper points out that when future uncertainties exist, the limitations and problems of decisive population projection should be examined, and the future population should be examined with probabilistic population prediction, and the results are presented. As a result of the analysis, in terms of the probabilistic confidence interval (5th quartile, 95th quartile), 5,106 to 51.2 million people in 2025, 5,053 to 5,082 million in 2030, 4,829 to 4,8 million in 2040, 4,425 to 45,5 million in 2050, and the last forecast, in 2062, the number below 40 million, is expected to be 37.33 to 33.3 million, and the rapid population deceleration over 33 years was the biggest factor rapidly decline in the fertility rate.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.1-16
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• 1999

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event system. The proposed algorithm in this paper searches the effective and reliable alternatives satisfying the target values of the system to be designed through a single run in a relatively short time period. It tries to estimate an autoregressive model, and construct mean and confidence interval for evaluating correctly the objective function obtained by small amount of output data. The experimental results using the proposed method are also shown.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.187-201
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• 2008

Earthquake induced hysteretic energy demand for a structure can be used as a limiting value of a certain performance level in seismic design of structures. In cases where it is larger than the hysteretic energy dissipation capacity of the structure, failure will occur. To be able to select the limiting value of hysteretic energy for a particular earthquake hazard level, it is required to define the variation of hysteretic energy in terms of probabilistic terms. This study focuses on the probabilistic evaluation of earthquake induced worst failure probability and approximate confidence intervals for inelastic single-degree-of-freedom (SDOF) systems with a typical steel moment connection based on hysteretic energy. For this purpose, hysteretic energy demand is predicted for a set of SDOF systems subject to an ensemble of moderate and severe EQGMs, while the hysteretic energy dissipation capacity is evaluated through the previously published cyclic test data on full-scale steel beam-to-column connections. The failure probability corresponding to the worst possible case is determined based on the hysteretic energy demand and dissipation capacity. The results show that as the capacity to demand ratio increases, the failure probability decreases dramatically. If this ratio is too small, then the failure is inevitable.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.50-55
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• 1998

In this paper we use discrete simulation method to get the criteria of system evaluation required in the case of designing the complicated probabilistic event system having discrete probabilistic variables and to search the effective and reliable alternatives to satisfy the objective value of the given system through on-line, single run within the short time period. If we find the alternative we construct the algorithm which change values of decision variables and determining alternative by using the stopping algorithm which end the simulation in the steady state of system. In order to prevent the loss of data when we analyze the acquired design alternative in the steady state we provide the background of the estimation of the autoregressive model and mean and confidence interval for evaluating correctly the objective function obtained by the small amount of output data through the short time period simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.11
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• pp.1099-1108
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• 2017

Data points obtained by conducting repetitive experiments under identical environmental conditions are, theoretically, required to correspond. However, experimental data often display variations due to generated errors or noise resulting from various factors and inherent uncertainties. In this study, an algorithm aiming to determine valid bounds of input variables, representing uncertainties, was developed using probabilistic and statistical methods. Furthermore, a reliability assessment was performed to verify and validate applications of this algorithm using bolt-fastening friction coefficient data in a sample application.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.73-79
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• 2005

This paper, introduces a Stochastic Project Scheduling Simulation system (SPSS III) developed by the author to predict a project completion probability in a certain time. The system integrates deterministic CPM, probabilistic PERT, and stochastic Discrete Event Simulation (DES) scheduling methods into one system. It implements automated statistical analysis methods for computing the minimum number of simulation runs, the significance of the difference between independent simulations, and the confidence interval for the mean project duration as well as sensitivity analysis method in What-if analyzer component. The SPSS 111 gives the several benefits to researchers in that it (1) complements PERT and Monte Carlo simulation by using stochastic activity durations via a web based JAVA simulation over the Internet, (2) provides a way to model a project network having different probability distribution functions, (3) implements statistical analyses method which enable to produce a reliable prediction of the probability of completing a project in a specified time, and (4) allows researchers to compare the outcome of CPM, PERT and DES under different variability or skewness in the activity duration data.

• Journal of the Korean Geotechnical Society
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• v.26 no.12
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• pp.71-79
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• 2010

The reliable evaluation of the coefficient of variation (COV) of soil properties is required for the determination of adequate design values and the application of a probabilistic method for the design of geotechnical structures. In this paper, the applicability of methods for estimating the standard deviation, such as the. Three-Sigma Rule and a statistical method, is evaluated by using site investigation data of the Songdo area. It is found that the Three-Sigma Rule provides similar results to those of a statistical method when using $N_{\sigma}$=6 for the property with small variability and $N_{\sigma}$=4.2~5.3 for the property with large variability. It is also observed that, for the undrained shear strength that has an increasing trend with depth, a $N_{\sigma}$ value of 4 is adequate for the evaluation of the variability by the Three-Sigma Rule. The COVs of soil properties determined in this paper could be used in the estimation of the confidence interval and characteristic values of soil properties.