• Title/Summary/Keyword: Interval Method

Search Result 2,929, Processing Time 0.026 seconds

Non-stochastic interval factor method-based FEA for structural stress responses with uncertainty

  • Lee, Dongkyu;Shin, Soomi
    • Structural Engineering and Mechanics
    • /
    • v.62 no.6
    • /
    • pp.703-708
    • /
    • 2017
  • The goal of this study is to evaluate behavior uncertainties of structures by using interval finite element analysis based on interval factor method as a specific non-stochastic tool. The interval finite element method, i.e., interval FEM, is a finite element method that uses interval parameters in situations where it is not possible to get reliable probabilistic characteristics of the structure. The present method solves the uncertainty problems of a 2D solid structure, in which structural characteristics are assumed to be represented as interval parameters. An interval analysis method using interval factors is applied to obtain the solution. Numerical applications verify the intuitive effectiveness of the present method to investigate structural uncertainties such as displacement and stress without the application of probability theory.

Interval finite element method based on the element for eigenvalue analysis of structures with interval parameters

  • Yang, Xiaowei;Chen, Suhuan;Lian, Huadong
    • Structural Engineering and Mechanics
    • /
    • v.12 no.6
    • /
    • pp.669-684
    • /
    • 2001
  • A new method for solving the uncertain eigenvalue problems of the structures with interval parameters, interval finite element method based on the element, is presented in this paper. The calculations are done on the element basis, hence, the efforts are greatly reduced. In order to illustrate the accuracy of the method, a continuous beam system is given, the results obtained by it are compared with those obtained by Chen and Qiu (1994); in order to demonstrate that the proposed method provides safe bounds for the eigenfrequencies, an undamping spring-mass system, in which the exact interval bounds are known, is given, the results obtained by it are compared with those obtained by Qiu et al. (1999), where the exact interval bounds are given. The numerical results show that the proposed method is effective for estimating the eigenvalue bounds of structures with interval parameters.

An improved interval analysis method for uncertain structures

  • Wu, Jie;Zhao, You Qun;Chen, Su Huan
    • Structural Engineering and Mechanics
    • /
    • v.20 no.6
    • /
    • pp.713-726
    • /
    • 2005
  • Based on the improved first order Taylor interval expansion, a new interval analysis method for the static or dynamic response of the structures with interval parameters is presented. In the improved first order Taylor interval expansion, the first order derivative terms of the function are also considered to be intervals. Combining the improved first order Taylor series expansion and the interval extension of function, the new interval analysis method is derived. The present method is implemented for a continuous beam and a frame structure. The numerical results show that the method is more accurate than the one based on the conventional first order Taylor expansion.

Interval Regression Models Using Variable Selection

  • Choi Seung-Hoe
    • Communications for Statistical Applications and Methods
    • /
    • v.13 no.1
    • /
    • pp.125-134
    • /
    • 2006
  • This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).

A Study on Evaluation Method of Fatigue Strength Data Using Likelihood Interval Estimation Method (우도구간 추정법에 의한 피로강도 데이터 평가법에 관한 연구)

  • 최창섭
    • Journal of the Korean Society of Safety
    • /
    • v.10 no.2
    • /
    • pp.10-16
    • /
    • 1995
  • In estimating the fatigue data, only the uniform safety rate has been applied so far However, since more reasonable design concepts such as machine structures or subsidiary materials will be required in the future, the importance of a statistical estimation method for fatigue data is being highlighted. With such basic conception in mind, this study was aimed at critically discussing the interval estimation method which has been applied using the classical statistics thus far It was conceived that this conventional method would result in the estimation of the unstable side from the viewpoint of the likelihood Interval estimation method. In this regard, this study aimed at estimating the fatigue strength through the likelihood interval estimation method comparing it with the conventional interval estimation method would result in the estimation of the unstable side from the viewpoint of the likelihood interval estimation method. One of the methods using the likelihood for estimation data is the Bayes method. Based on this theory, statistical estimations were positivly applied, and thereupon, the fatigue data were estimated.

  • PDF

On Estimation of HPD Interval for the Generalized Variance Using a Weighted Monte Carlo Method

  • Kim, Hea-Jung
    • Communications for Statistical Applications and Methods
    • /
    • v.9 no.2
    • /
    • pp.305-313
    • /
    • 2002
  • Regarding to inference about a scalar measure of internal scatter of Ρ-variate normal population, this paper considers an interval estimation of the generalized variance, │$\Sigma$│. Due to complicate sampling distribution, fully parametric frequentist approach for the interval estimation is not available and thus Bayesian method is pursued to calculate the highest probability density (HPD) interval for the generalized variance. It is seen that the marginal posterior distribution of the generalized variance is intractable, and hence a weighted Monte Carlo method, a variant of Chen and Shao (1999) method, is developed to calculate the HPD interval of the generalized variance. Necessary theories involved in the method and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed method.

Confidence Interval for Capability Process Indices by the Resampling Method (재표집방법에 의한 공정관리지수의 신뢰구간)

  • 남경현
    • Journal of Applied Reliability
    • /
    • v.1 no.1
    • /
    • pp.55-63
    • /
    • 2001
  • In this paper, we utilize the asymptotic variance of $C_{pk}$ to propose a two-sided confidence interval based on percentile-t bootstrap method. This confidence interval is compared with the ones based on the standard and percentile bootstrap methods. Simulation results show that percentile-t bootstrap method is preferred to other methods for constructing the confidence interval.l.

  • PDF

An iterative hybrid random-interval structural reliability analysis

  • Fang, Yongfeng;Xiong, Jianbin;Tee, Kong Fah
    • Earthquakes and Structures
    • /
    • v.7 no.6
    • /
    • pp.1061-1070
    • /
    • 2014
  • An iterative hybrid structural dynamic reliability prediction model has been developed under multiple-time interval loads with and without consideration of stochastic structural strength degradation. Firstly, multiple-time interval loads have been substituted by the equivalent interval load. The equivalent interval load and structural strength are assumed as random variables. For structural reliability problem with random and interval variables, the interval variables can be converted to uniformly distributed random variables. Secondly, structural reliability with interval and stochastic variables is computed iteratively using the first order second moment method according to the stress-strength interference theory. Finally, the proposed method is verified by three examples which show that the method is practicable, rational and gives accurate prediction.

Decision-making Method of Optimum Inspection Interval for Plant Maintenance by Genetic Algorithms (유전 알고리즘에 의한 플랜트 보전을 위한 최적검사기간 결정 방법론)

  • 서광규;서지한
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.26 no.2
    • /
    • pp.1-8
    • /
    • 2003
  • The operation and management of a plant require proper accounting for the constraints coming from reliability requirements as well as from budget and resource considerations. Most of the mathematical methods to decide the inspection time interval for plant maintenance by reliability theory are too complicated to be solved. Moreover, the mathematical and theoretical models are not usually cases in the practical applications. In order to overcome these problems, we propose a new the decision-making method of optimal inspection interval to minimize the maintenance cost by reliability theory and genetic algorithm (GA). The most merit of the proposed method is to decide the inspection interval for a plant machine of which failure rate $\lambda$(t) conforms to any probability distribution. Therefore, this method is more practical. The efficiency of the proposed method is verified by comparing the results obtained by GA-based method with the inspection model haying regular time interval.

THE RELIABLE MODIFIED OF LAPLACE ADOMIAN DECOMPOSITION METHOD TO SOLVE NONLINEAR INTERVAL VOLTERRA-FREDHOLM INTEGRAL EQUATIONS

  • Hamoud, Ahmed A.;Ghadle, Kirtiwant P.
    • Korean Journal of Mathematics
    • /
    • v.25 no.3
    • /
    • pp.323-334
    • /
    • 2017
  • In this paper, we propose a combined form for solving nonlinear interval Volterra-Fredholm integral equations of the second kind based on the modifying Laplace Adomian decomposition method. We find the exact solutions of nonlinear interval Volterra-Fredholm integral equations with less computation as compared with standard decomposition method. Finally, an illustrative example has been solved to show the efficiency of the proposed method.