• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.77-87
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• 2012

Recently, Load and Resistance Factor Design (LRFD) based on reliability analysis has become a global trend for economical and rational design. In order to implement the LRFD, quantification of uncertainty for load and resistance should be done. The reliability of result relies on input variable, and therefore, it is important to obtain exact uncertainty properties of load and resistance. Since soil stress is the main reason causing the settlement or deformation of ground and load on the underground structure, it is essential to clarify the uncertainty of soil stress distribution for accurately predict the uncertainty of load in LRFD. In this study, laboratory model test on silty sand bed under probabilistic load is performed to observe propagation of upper load uncertainty. The results show that the coefficient of variation (COV) of soil stress are varied depending on location due to non-linear relationship between upper load increment and soil pressure increment. In addition, when the load uncertainty is transmitted through ground, COV is decreased by damping effect.

• Geomechanics and Engineering
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• v.11 no.2
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• pp.211-234
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• 2016

A total of 99 full-scale field load tests at 22 sites were compiled for this study to elucidate several issues related to the load-displacement behaviour of belled piers under axial uplift loading, including (1) interpretation criteria to define various elastic, inelastic, and "failure" states for each load test from the load-displacement curve; (2) generalized correlations among these states and determinations to the predicted ultimate uplift resistances; (3) uncertainty in the resistance model factor statistics required for reliability-based ultimate limit state (ULS) design; (4) uncertainty associated with the normalized load-displacement curves and the resulting model factor statistics required for reliability-based serviceability limit state (SLS) design; and (5) variations of the combined ULS and SLS model factor statistics for reliability-based limit state designs. The approaches discussed in this study are practical and grounded realistically on the load tests of belled piers with minimal assumptions. The results on the characterization and uncertainty of uplift load-displacement behaviour of belled piers could be served as to extend the early contributions for reliability-based ULS and SLS designs.

• Ocean Systems Engineering
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• v.6 no.2
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• pp.191-201
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• 2016

Stern bearing is a key component of marine propulsion plant. Its environment is diverse, working condition changeable, and condition severe, so that stern bearing load is of strong time variability, which directly affects the safety and reliability of the system and the normal navigation of ships. In this paper, three affecting factors of the stern bearing load such as hull deformation, propeller hydrodynamic vertical force and bearing wear are calculated and characterized by random theory. The uncertainty mathematical model of stern bearing load is established to research the relationships between factors and uncertainty load of stern bearing. The validity of calculation mathematical model and results is verified by examples and experiment yet. Therefore, the research on the uncertainty load of stern bearing has important theoretical significance and engineering practical value.

• Structural Engineering and Mechanics
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• v.76 no.4
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• pp.529-540
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• 2020

Concurrent topology optimization of macrostructure and microstructure has attracted significant interest due to its high structural performance. However, most of the existing works are carried out under deterministic conditions, the obtained design may be vulnerable or even cause catastrophic failure when the load position exists uncertainty. Therefore, it is necessary to take load position uncertainty into consideration in structural design. This paper presents a computational method for robust concurrent topology optimization with consideration of load position uncertainty. The weighted sum of the mean and standard deviation of the structural compliance is defined as the objective function with constraints are imposed to both macro- and micro-scale structure volume fractions. The Bivariate Dimension Reduction method and Gauss-type quadrature (BDRGQ) are used to quantify and propagate load uncertainty to calculate the objective function. The effective properties of microstructure are evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The bi-directional evolutionary structural optimization (BESO) method is used to obtain the black-and-white designs. Several 2D and 3D examples are presented to validate the effectiveness of the proposed robust concurrent topology optimization method.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.139-141
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• 2005

For the reliable evaluation of the distribution and power transformer no-load loss measurement, measurement uncertainty of the transformer measuring system, consisted of current transformer, potential transformer and power metering equipment is required. In this paper, we describe the uncertainty of transformer measuring system based on ANSI/IEEE C57.12.90.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.161-167
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• 1999

This paper describes the calibration method and the calculation equations of expanded uncertainty for a precision electric force measuring device. The calibration of the electric force measuring device is performed three times (0 ${\circ}$(first time), $120{\circ}$(second time), $240{\circ}$(third time)) at each calibration point. It is usually selected ten points from zero load to rated load of the electric force measuring device. The expanded uncertainty is calculated by combining A type standard uncertainty and B type standard uncertainty. The calibration method and the calculation equations of expanded uncertainty can be widely used in the calibration of the precision electric force measuring device.

• Smart Structures and Systems
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• v.15 no.3
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• pp.751-767
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• 2015

A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.111-112
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• 2008

Although two types of uncertainty such as randomness and fuzziness simultaneously exist in power systems, yet they have been treated as distinct fields to evaluate the power system reliability. Thus, this paper presents a reliability assessment method based on a combined concept of fuzzy and probability. To reflect the two-fold uncertainty, a modified load duration curve(MLDC) is proposed using the probability distribution of historical load data in which a fuzzy model for the peak load forecast is embedded. IEEE RTS system was used to demonstrate the usefulness and applicability of the proposed method, and the reliability indices were obtained using the proposed MLDC. The results show a wider insight into impact of load fuzziness on uncertainties of reliability indices for power systems.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.562-564
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• 1995

This paper proposes a new algorithm for maintenance scheduling with considering load forecast uncertainty. The proposed algorithm is based on the equivalent load of effective load carrying capability(ELCC) of generators. The uncertainty of forecasted load is considered as a normal distribution probability density function. For maintenance scheduling, reserve levelization method and risk levelization method are used in this study. To test the algorithm, we applied the proposed method to IEEE reliability test system(IEEE RTS). As a result, we verified the validity of the proposed method.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.402-405
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• 1991

This paper represents a new method for computing reliability indices by using Large Deviation method which is one of the probabilistic production cost simulations. The reliability measures are based on the models used for the loads and for the generating unit failure states. In computing these measures it has been tacitly assumed that the values of all parameters in the models are precisely known. In fact, however, some of these values must often be chosen with a considerable degree of uncertainty involved. This is particularly true for the forecast peak loads in the load model, where there is an inherent uncertainty in the method of forecasting, which are frequently based on insufficient statistics. In this paper, the effect of load forecasting uncertainty on the LOLP(Loss of Load Probability), is investigated. By applying the Large Deviation method to the IEEE Rilability Test System, it is verified that the proposed method is generally very accurate and very fast for computing system reliability indices.