• Structural Engineering and Mechanics
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• v.42 no.2
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• pp.175-189
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• 2012

This paper presents an algorithm for structural reliability with the response surface method. For this aim, an approach with three stages is proposed named as improved response surface method. In the algorithm, firstly, a quadratic approximate function is formed and design point is determined with First Order Reliability Method. Secondly, a point close to the exact limit state function is searched using the design point. Lastly, vector projected method is used to generate the sample points and Second Order Reliability Method is performed to obtain reliability index and probability of failure. Five numerical examples are selected to illustrate the proposed algorithm. The limit state functions of three examples (cantilever beam, highly nonlinear limit state function and dynamic response of an oscillator) are defined explicitly and the others (frame and truss structures) are defined implicitly. ANSYS finite element program is utilized to obtain the response of the structures which are needed in the reliability analysis of implicit limit state functions. The results (reliability index, probability of failure and limit state function evaluations) obtained from the improved response surface are compared with those of Monte Carlo Simulation, First Order Reliability Method, Second Order Reliability Method and Classical Response Surface Method. According to the results, proposed algorithm gives better results for both reliability index and limit state function evaluations.

• Structural Engineering and Mechanics
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• v.62 no.3
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• pp.365-372
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• 2017

Reliability analysis is a probabilistic approach to determine a safety level of a system. Reliability is defined as a probability of a system (or a structure, in structural engineering) to functionally perform under given conditions. In the 1960s, Basler defined the reliability index as a measure to elucidate the safety level of the system, which until today is a commonly used parameter. However, the reliability index has been formulated based on the pivotal assumption which assumed that the considered limit state function is normally distributed. Nevertheless, it is not guaranteed that the limit state function of systems follow as normal distributions; therefore, there is a need to define a new reliability index for no-normal distributions. The main contribution of this paper is to define a sophisticated reliability index for limit state functions which their distributions are non-normal. To do so, the new definition of reliability index is introduced for non-normal limit state functions according to the probability functions which are calculated based on the convolution theory. Eventually, as the state of the art, this paper introduces a simplified method to calculate the reliability index for non-normal distributions. The simplified method is developed to generate non-normal limit state in terms of normal distributions using series of Gaussian functions.

• Structural Engineering and Mechanics
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• v.38 no.5
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• pp.651-674
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• 2011

This paper presents a novel method for predicting the failure probability of structural or mechanical systems subjected to random loads and material properties involving multiple design points. The method involves Multicut High Dimensional Model Representation (Multicut-HDMR) technique in conjunction with moving least squares to approximate the original implicit limit state/performance function with an explicit function. Depending on the order chosen sometimes truncated Cut-HDMR expansion is unable to approximate the original implicit limit state/performance function when multiple design points exist on the limit state/performance function or when the problem domain is large. Multicut-HDMR addresses this problem by using multiple reference points to improve accuracy of the approximate limit state/performance function. Numerical examples show the accuracy and efficiency of the proposed approach in estimating the failure probability.

• Structural Engineering and Mechanics
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• v.47 no.1
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• pp.45-58
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• 2013

In structural reliability analysis, the response surface method is a powerful method to evaluate the probability of failure. However, the location of experimental points used to form a response surface function must be selected in a judicious way. It is necessary for the highly nonlinear limit state functions to consider the design point and the nonlinear trend of the limit state, because both of them influence the probability of failure. In this paper, in order to approximate the actual limit state more accurately, experimental points are selected close to the design point and the actual limit state, and consider the nonlinear trend of the limit state. Linear, quadratic and cubic polynomials without mixed terms are utilized to approximate the actual limit state. The direct Monte Carlo simulation on the approximated limit state is carried out to determine the probability of failure. Four examples are given to demonstrate the efficiency and the accuracy of the proposed method for both numerical and implicit limit states.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.3
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• pp.183-189
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• 2013

In this paper, uncertainties and failure criteria of structure are mathematically expressed by random variables and a limit state equation. A limit state equation is approximated by Fleishman's 3rd order polynomials and the theoretical moments of an approximated limit state equation are calculated. Fleishman introduced a 3rd order polynomial in terms of only standard normal distiribution random variables. But, in this paper, Fleishman's polynomial is extended to various random variables including beta, gamma, uniform distributions. Cumulants and a normalized limit state equation are used to calculate a theoretical moments of a limit state equation. A cumulative distribution function of a normalized limit state equation is approximated by a Pearson system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.672-676
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• 2002

In this paper, a reliability-based design optimization method, which enables the determination of optimum design that incorporate confidence range for structures, is studied. Response surface method and Monte Carlo simulation are utilized to determine limit state function. The proposed method is applied to the I-type steel structure for reliability based optimal design.

• Journal of Electrical Engineering and Technology
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• v.6 no.5
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• pp.697-705
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• 2011

In this paper, a closed-loop system constructed with a linear plant and nonlinearity in the feedback connection is considered to argue against its planar orbital stability. Through a state space approach, a main result that presents a sufficient stability criterion of the limit cycle predicted by solving the harmonic balance equation is given. Preliminarily, the harmonic balance of the nonlinear feedback loop is assumed to have a solution that determines the characteristics of the limit cycle. Using a state-space approach, the nonlinear loop equation is reformulated into a linear perturbed model through the introduction of a residual operator. By considering a series of transformations, such as a modified eigenstructure decomposition, periodic averaging, change of variables, and coordinate transformation, the stability of the limit cycle can be simply tested via a scalar function and matrix. Finally, the stability criterion is addressed by constructing a composite Lyapunov function of the transformed system.

• Wind and Structures
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• v.27 no.3
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• pp.149-161
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• 2018

A reliability analysis method is proposed in this paper based on the maximum entropy (MaxEnt) principle in which constraints are specified in terms of the fractional moments instead of integer moments. Then a multiplicative dimensional reduction method (M-DRM) is introduced to compute the fractional moments. The method is applicable for both explicit and implicit limit state functions of complex structures. After two examples illustrate the accuracy and efficiency of this method in comparison to the Monte Carlo simulation (MCS), the method is used to analyze the flutter reliability of suspension bridge. The results show that the empirical formula method in which the limit state function is explicitly represented as a function of variables is only a too conservative estimate for flutter reliability analysis but is not accurate adequately. So it is not suitable for reliability analysis of bridge flutter. The actual flutter reliability analysis should be conducted based on a finite element method in which limit state function is implicitly represented as a function of variables. The proposed M-DRM provide an alternate and efficient way to analyze a much more complicated flutter reliability of long span suspension bridge.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.497-505
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• 2003

Under normality assumption, the tolerance interval for a future observation is sometimes of great interest in statistics. In this paper, we state the influence function on the standard deviation $\sigma$, and use it to derive the influence function on tolerance limits. Simulation study shows that the two influence functions perform very well.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.89-94
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• 2003

Rolling stock structures such as bogie frame and car body play an important role for the support of vehicle leading. In general, more than 25 years' durability is needed for them. A lot of study has been carried out for the prediction of the fatigue life of the bogie frame and car body in experimental and theoretical domains. One of the new methods is a probabilistic fatigue lift evaluation. The objective of this paper is to estimate the fatigue lift of the bogie frame of an electric car, which was developed by the Korea Railroad Research Institute (KRRI). We used two approaches. In the first approach probabilistic distribution of S-N curve and limit state function of the equivalent stress of the measured stress spectra are used. In the second approach, limit state function is also used. And load spectra measured by strain gauges are approximated by the two-parameter Weibull distribution. Other probabilistic variables are represented by log-normal and normal distributions. Finally, reliability index and structural integrity of the bogie frame are estimated.