An orthogonal normal transformation of correlated non-normal random variables for structural reliability

2021 ◽  
Vol 64 ◽  
pp. 103130
Author(s):  
Yan-Gang Zhao ◽  
Ye-Yao Weng ◽  
Zhao-Hui Lu
Keyword(s):  
Structural Reliability ◽  
Random Variables ◽  
Normal Transformation

scholarly journals Hermite polynomial normal transformation for structural reliability analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinsheng Wang ◽  
Muhannad Aldosary ◽  
Song Cen ◽  
Chenfeng Li
Keyword(s):  
Reliability Analysis ◽  
Hermite Polynomial ◽  
Structural Reliability ◽  
Hermite Polynomials ◽  
Random Variables ◽  
Transformation Method ◽  
Statistical Moments ◽  
Content Type ◽  
Structural Reliability Analysis ◽  
Normal Transformation

Purpose Normal transformation is often required in structural reliability analysis to convert the non-normal random variables into independent standard normal variables. The existing normal transformation techniques, for example, Rosenblatt transformation and Nataf transformation, usually require the joint probability density function (PDF) and/or marginal PDFs of non-normal random variables. In practical problems, however, the joint PDF and marginal PDFs are often unknown due to the lack of data while the statistical information is much easier to be expressed in terms of statistical moments and correlation coefficients. This study aims to address this issue, by presenting an alternative normal transformation method that does not require PDFs of the input random variables. Design/methodology/approach The new approach, namely, the Hermite polynomial normal transformation, expresses the normal transformation function in terms of Hermite polynomials and it works with both uncorrelated and correlated random variables. Its application in structural reliability analysis using different methods is thoroughly investigated via a number of carefully designed comparison studies. Findings Comprehensive comparisons are conducted to examine the performance of the proposed Hermite polynomial normal transformation scheme. The results show that the presented approach has comparable accuracy to previous methods and can be obtained in closed-form. Moreover, the new scheme only requires the first four statistical moments and/or the correlation coefficients between random variables, which greatly widen the applicability of normal transformations in practical problems. Originality/value This study interprets the classical polynomial normal transformation method in terms of Hermite polynomials, namely, Hermite polynomial normal transformation, to convert uncorrelated/correlated random variables into standard normal random variables. The new scheme only requires the first four statistical moments to operate, making it particularly suitable for problems that are constraint by limited data. Besides, the extension to correlated cases can easily be achieved with the introducing of the Hermite polynomials. Compared to existing methods, the new scheme is cheap to compute and delivers comparable accuracy.

Structural Reliability Analysis Using Fourier Orthogonal Neural Network Method

2013 ◽  
Vol 838-841 ◽  
pp. 360-363 ◽  
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Failure Probability ◽  
Structural Reliability ◽  
Random Variables ◽  
Structural Reliability Analysis ◽  
Surface Method ◽  
Structural Responses

In order to predict the failure probability of a complicated structure, the structural responses usually need to be predicted by a numerical procedure, such as FEM method. The response surface method could be used to reduce the computational effort required for reliability analysis. However the conventional response surface method is still time consuming when the number of random variables is large. In this paper, a Fourier orthogonal neural network (FONN)-based response surface method is proposed. In this method, the relationship between the random variables and structural responses is established using FONN models. Then the FONN model is connected to the first order and second moment method (FORM) to predict the failure probability. Numerical example result shows that the proposed approach is efficient and accurate, and is applicable to structural reliability analysis.

Neural network approach to model the limit state surface for reliability analysis

2003 ◽  
Vol 40 (6) ◽  
pp. 1235-1244 ◽  
Author(s):  
Anthony TC Goh ◽  
Fred H Kulhawy
Keyword(s):  
Neural Network ◽  
Reliability Index ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
First Order Reliability Method ◽  
First Order ◽  
The Neural Network ◽  
Reliability Method ◽  
Geotechnical Structures

Structural reliability methods are often used to evaluate the failure performance of geotechnical structures. A common approach is to use the first-order reliability method. Its popularity results from the mathematical simplicity of the method, since only second moment information (mean and coefficient of variation) on the random variables is required. The probability of failure is then assessed by an index known commonly as the reliability index. One critical aspect in determining the reliability index is the explicit definition of the limit state surface of the system. In a problem involving multi-dimensional random variables, the limit state surface is the boundary separating the safe domain from the "failure" (or lack of serviceability) domain. In many complicated and nonlinear problems where the analyses involve the use of numerical procedures such as the finite element method, this surface may be difficult to determine explicitly in terms of the random variables, and therefore the limit state can only be expressed implicitly rather than in a closed-form solution. It is proposed in this paper to use an artificial intelligence technique known as the back-propagation neural network algorithm to model the limit state surface. First, the failure domain is found through repeated point-by-point numerical analyses with different input values. The neural network is then trained on this set of data. Using the optimal weights of the neural network connections, it is possible to develop a mathematical expression relating the input and output variables that approximates the limit state surface. Some examples are given to illustrate the application and accuracy of the proposed approach.Key words: first-order reliability method, geotechnical structures, limit state surface, neural networks, reliability.

STRUCTURAL RELIABILITY ANALYSIS OF STEEL TRUSS ELEMENTS ON BUCKLING USING PBOX APPROACH

2021 ◽  
pp. 45-53
Author(s):  
A.A. Solovyova ◽  
◽  
S.A. Solovyov ◽  
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Statistical Data ◽  
Limit State ◽  
Random Variables ◽  
Distribution Functions ◽  
Structural Safety ◽  
Random Load ◽  
Load Models ◽  
Steel Trusses

Abstract. The reliability of load-bearing structural elements is one of the indicators of structural safety. The article presents methods for steel trusses bars reliability analysis according to the buckling criterion using p-boxes. A p-box consists of two boundary probability distribution functions that form the area of possible distribution functions. Such model used for modeling random variables in conditions of incomplete statistical data by quantity or quality. An algorithm for summing p-boxes of random load models is demonstrated on the example of a probabilistic estimate of the force in the truss bar. The result of reliability analysis using p-boxes is presented in interval form. The use of p-boxes makes it possible to obtain a more cautious assessment of reliability in case of incomplete statistical data. To increase the informativity of the reliability analysis result, it is necessary to obtain more statistical data about random variables in design mathematical models of limit state, which will allow forming p-boxes with narrower boundary distribution functions.

Iteration Algorithm for Propagation of Mixed Uncertainties in Reliability Analysis

2011 ◽  
Vol 199-200 ◽  
pp. 569-574
Author(s):  
Chang Cong Zhou ◽  
Zhen Zhou Lu ◽  
Qi Wang
Keyword(s):  
Structural Reliability ◽  
Random Variables ◽  
Point Estimate ◽  
Iteration Algorithm ◽  
Fourth Moment ◽  
Performance Function ◽  
Fuzzy Variables ◽  
Point Estimate Method ◽  
Interval Variables ◽  
Reliability Method

For structural reliability problems simultaneously involving random variables, interval variables and fuzzy variables, an iteration algorithm is proposed to deal with the propagation of uncertainties. Corresponding to assumed membership value in the membership level interval [0,1], the membership interval of fuzzy variables can be obtained. After the fuzzy variables and the interval variables’ effects on the extreme value of performance function are alternately and iteratively analyzed with the random variables’ effects on statistical properties of the performance function, converged design point can be calculated, and the reliability can be as well calculated by the fourth moment algorithm based on the point estimate method. Finally the membership function of the reliability can be solved. Owing to the faster convergence of the iteration algorithm, the efficiency of the proposed algorithm is highly improved compared to the conventional numerical simulation method. And the adoption of fourth moment method proves much better in accuracy than only applying first order reliability method in the iteration algorithm. After the basic concept and process of the proposed algorithm is detailed, several numerical and engineering examples are studied to demonstrate its advantage both in efficiency and accuracy.

scholarly journals Hybrid Reliability Analysis for Spacecraft Docking Lock with Random and Interval Uncertainty

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jianguo Zhang ◽  
Jiwei Qiu ◽  
Pidong Wang
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
Simple Problem ◽  
State Function ◽  
Limit State Function ◽  
Interval Variables ◽  
Reliability Method ◽  
Hybrid Reliability

This paper presents a novel procedure based on first-order reliability method (FORM) for structural reliability analysis with hybrid variables, that is, random and interval variables. This method can significantly improve the computational efficiency for the abovementioned hybrid reliability analysis (HRA), while generally providing sufficient precision. In the proposed procedure, the hybrid problem is reduced to standard reliability problem with the polar coordinates, where an n-dimensional limit-state function is defined only in terms of two random variables. Firstly, the linear Taylor series is used to approximate the limit-state function around the design point. Subsequently, with the approximation of the n-dimensional limit-state function, the new bidimensional limit state is established by the polar coordinate transformation. And the probability density functions (PDFs) of the two variables can be obtained by the PDFs of random variables and bounds of interval variables. Then, the interval of failure probability is efficiently calculated by the integral method. At last, one simple problem with explicit expressions and one engineering application of spacecraft docking lock are employed to demonstrate the effectiveness of the proposed methods.

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