• Steel and Composite Structures
• /
• v.19 no.4
• /
• pp.811-827
• /
• 2015

This paper presents a probabilistic investigation of American and European specifications (i.e., AISC and Eurocode 4) for square concrete-filled steel tubular (CFT) stub columns. The study is based on experimental results of 100 axially loaded square CFT stub columns from the literature. By comparing experimental results for ultimate loads with code-predicted column resistances, the uncertainty of resistance models is analyzed and it is found that the modeling uncertainty parameter can be described using random variables of lognormal distribution. Reliability analyses were then performed with/without considering the modeling uncertainty parameter and the safety level of the specifications is evaluated in terms of sufficient and uniform reliability criteria. Results show that: (1) The AISC design code provided slightly conservative results of square CFT stub columns with reliability indices larger than 3.25 and the uniformness of reliability indices is no better because of the quality of the resistance model; (2) The uniformness of reliability indices for the Eurocode 4 was better than that of AISC, but the reliability indices of columns designed following the Eurocode 4 were found to be quite below the target reliability level of Eurocode 4.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.1
• /
• pp.9-16
• /
• 1997

The method of designing robust two degree of freedom(2 DOF) controllers for linear systems with parameter uncertainties and unmodeled dynamics is presented in this paper. Robust performance condition that accounts for robust model matching of closed loop system and disturbance rejection is derived. Using the robust performance condition, the feedback controller is designed to meet robust stability and disturbance rejection specifications, while prefilter is used to improve the robust model matching properties. The $H^{\infty}$ and $\mu$ controller for six degree of freedom vehicle with parameter variations are designed and compared. Simulations for hydrodynamic parameter variations and disturbance are presented to demonstrate the achievement of good robust performance.

• Journal of Information Processing Systems
• /
• v.15 no.2
• /
• pp.331-343
• /
• 2019

Dynamic thermal rating of the overhead transmission lines is affected by many uncertain factors. The ambient temperature, wind speed and wind direction are the main sources of uncertainty. Measurement uncertainty is an important parameter to evaluate the reliability of measurement results. This paper presents the uncertainty analysis based on Monte Carlo. On the basis of establishing the mathematical model and setting the probability density function of the input parameter value, the probability density function of the output value is determined by probability distribution random sampling. Through the calculation and analysis of the transient thermal balance equation and the steady- state thermal balance equation, the steady-state current carrying capacity, the transient current carrying capacity, the standard uncertainty and the probability distribution of the minimum and maximum values of the conductor under 95% confidence interval are obtained. The simulation results indicate that Monte Carlo method can decrease the computational complexity, speed up the calculation, and increase the validity and reliability of the uncertainty evaluation.

• Proceedings of the KSR Conference
• /
• 2003.10c
• /
• pp.301-306
• /
• 2003

Recently, uncertainty of measurement became a major concern for the people working on the laboratory evaluation and accreditation. 'uncertainty of measurement is a parameter associated with the result of a measurement that characteristics the dispersion of the value that could reasonably be attributed to the measured.' This study analysed how to estimate uncertainty of measurement in mechanical characteristic exam for Plastic material. its uncertainty was estimated according to International Organization for Standardization(ISO), they were named to A type uncertainty, B type uncertainty, combined standard uncertainty, and expanded uncertainty. We obtained that the combined standard uncertainty was 0.96697 MPa and the expanded uncertainty was 2.291MPa.

• Journal of Korean Society on Water Environment
• /
• v.36 no.5
• /
• pp.373-384
• /
• 2020

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

• Journal of Korean Society on Water Environment
• /
• v.29 no.5
• /
• pp.681-690
• /
• 2013

Watershed models have been increasingly used to support an integrated management of land and water, non-point source pollutants, and implement total daily maximum load policy. However, these models demand a great amount of input data, process parameters, a proper calibration, and sometimes result in significant uncertainty in the simulation results. For this reason, uncertainty analysis is necessary to minimize the risk in the use of the models for an important decision making. The objectives of this study were to evaluate three different uncertainty analysis algorithms (SUFI-2: Sequential Uncertainty Fitting-Ver.2, GLUE: Generalized Likelihood Uncertainty Estimation, ParaSol: Parameter Solution) that used to analyze the sensitivity of the SWAT(Soil and Water Assessment Tool) parameters and auto-calibration in a watershed, evaluate the uncertainties on the simulations of runoff and sediment load, and suggest alternatives to reduce the uncertainty. The results confirmed that the parameters which are most sensitive to runoff and sediment simulations were consistent in three algorithms although the order of importance is slightly different. In addition, there was no significant difference in the performance of auto-calibration results for runoff simulations. On the other hand, sediment calibration results showed less modeling efficiency compared to runoff simulations, which is probably due to the lack of measurement data. It is obvious that the parameter uncertainty in the sediment simulation is much grater than that in the runoff simulation. To decrease the uncertainty of SWAT simulations, it is recommended to estimate feasible ranges of model parameters, and obtain sufficient and reliable measurement data for the study site.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.5 no.4
• /
• pp.337-348
• /
• 2003

The propagation mechanism of a detonation pressure with fully coupled charge is clarified and the blasting pressure propagated in rock mass is derived from the application of shock wave theory. The blasting pressure was a function of detonation velocity, isentropic exponent, explosive density, Hugoniot parameters, and rock density. Probabilistic distribution is obtained by using explosion tests on emulsion and rock property tests on granite in Seoul and then the probabilistic distribution of the blasting pressure is derived from the above mentioned properties. The probabilistic distributions of explosive properties and rock properties show a normal distribution so that the blasting pressure propagated in rock can be also regarded as a normal distribution. Parametric analysis was performed to pinpoint the most influential parameter that affects the blasting pressure and it was found that the detonation velocity is the most sensitive parameter. Moreover, uncertainty analysis was performed to figure out the effect of each parameter uncertainty on the uncertainty of blasting pressure. Its result showed that uncertainty of natural rock properties constitutes the main portion of blasting pressure uncertainty rather than that of explosive properties. In other words, since rock property uncertainty is much larger than detonation velocity uncertainty the blasting pressure uncertainty is more influenced by the former than by the latter even though the detonation velocity is found to be the most influencing parameter on the blasting pressure.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1990.04a
• /
• pp.272-288
• /
• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.332-332
• /
• 2000

In this paper, a novel sliding mode control with uncertainty adaptation is produced by introducing a virtual state. Because upper bounds of the uncertainty is difficult to know, we estimate these upper bound by using the simple adaptation law and design the novel sliding mode controller. The nominal controller is used the optimal controller to minimize cost function.

• Journal of Korea Water Resources Association
• /
• v.51 no.6
• /
• pp.523-533
• /
• 2018

The objective of this study is to analyze uncertainties of ensemble-based streamflow prediction method for model parameters and input data. ESP (Ensemble Streamflow Prediction) and BAYES-ESP (Bayesian-ESP) based on ABCD rainfall-runoff model were selected as streamflow prediction method. GLUE (Generalized Likelihood Uncertainty Estimation) was applied for the analysis of parameter uncertainty. The analysis of input uncertainty was performed according to the duration of meteorological scenarios for ESP. The result showed that parameter uncertainty was much more significant than input uncertainty for the ensemble-based streamflow prediction. It also indicated that the duration of observed meteorological data was appropriate to using more than 20 years. And the BAYES-ESP was effective to reduce uncertainty of ESP method. It is concluded that this analysis is meaningful for elaborating characteristics of ESP method and error factors of ensemble-based streamflow prediction method.