First Order Reliability Method With Truncated Random Variables

2012 ◽  
Vol 134 (9) ◽  
Author(s):  
Xiaoping Du ◽  
Zhen Hu
Keyword(s):  
Order Approximation ◽  
Probability Distributions ◽  
Computational Cost ◽  
Random Variables ◽  
Modify Form ◽  
Original Form ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method ◽  
Standard Normal

In many engineering applications, the probability distributions of some random variables are truncated; these truncated distributions are resulted from restricting the domain of other probability distributions. If the first order reliability method (FORM) is directly used, the truncated random variables will be transformed into unbounded standard normal distributions. This treatment may result in large errors in reliability analysis. In this work, we modify FORM so that the truncated random variables are transformed into truncated standard normal variables. After the first order approximation and variable transformation, saddlepoint approximation is then used to estimate the reliability. Without increasing the computational cost, the proposed method is generally more accurate than the original FORM for problems with truncated random variables.

Reliability Methods for Bimodal Distribution With First-Order Approximation1

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Probability Density ◽  
Order Approximation ◽  
Limit State ◽  
Random Variables ◽  
Random Variable ◽  
State Function ◽  
First Order ◽  
Reliability Method ◽  
Reliability Methods ◽  
Basic Random Variable

In traditional reliability problems, the distribution of a basic random variable is usually unimodal; in other words, the probability density of the basic random variable has only one peak. In real applications, some basic random variables may follow bimodal distributions with two peaks in their probability density. When binomial variables are involved, traditional reliability methods, such as the first-order second moment (FOSM) method and the first-order reliability method (FORM), will not be accurate. This study investigates the accuracy of using the saddlepoint approximation (SPA) for bimodal variables and then employs SPA-based reliability methods with first-order approximation to predict the reliability. A limit-state function is at first approximated with the first-order Taylor expansion so that it becomes a linear combination of the basic random variables, some of which are bimodally distributed. The SPA is then applied to estimate the reliability. Examples show that the SPA-based reliability methods are more accurate than FOSM and FORM.

First-Order Reliability Method for Structural Reliability Analysis in the Presence of Random and Interval Variables

2015 ◽  
Vol 1 (4) ◽  
Author(s):  
Umberto Alibrandi ◽  
C. G. Koh
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Limit State ◽  
Computational Cost ◽  
Computational Effort ◽  
Stochastic Dynamic ◽  
First Order Reliability Method ◽  
First Order ◽  
Structural Reliability Analysis ◽  
Reliability Method

This paper presents a novel procedure based on first-order reliability method (FORM) for structural reliability analysis in the presence of random parameters and interval uncertain parameters. In the proposed formulation, the hybrid problem is reduced to standard reliability problems, where the limit state functions are defined only in terms of the random variables. Monte Carlo simulation (MCS) for hybrid reliability analysis (HRA) is presented, and it is shown that it requires a tremendous computational effort; FORM for HRA is more efficient but still demanding. The computational cost is significantly reduced through a simplified procedure, which gives good approximations of the design points, by requiring only three classical FORMs and one interval analysis (IA), developed herein through an optimization procedure. FORM for HRA and its simplified formulation achieve a much improved efficiency than MCS by several orders of magnitude, and it can thus be applied to real-world engineering problems. Representative examples of stochastic dynamic analysis and performance-based engineering are presented.

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.

Reliability analysis for a spar-supported floating offshore wind turbine

2017 ◽  
Vol 42 (1) ◽  
pp. 51-65 ◽  
Author(s):  
Abhinav Sultania ◽  
Lance Manuel
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Reliability Analysis ◽  
Wave Height ◽  
Return Period ◽  
Random Variables ◽  
Environmental Data ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method

The reliability analysis of a spar-supported floating offshore 5-MW wind turbine is the subject of this study. Environmental data from a selected site are employed in the numerical studies. Using time-domain simulations, the dynamic behavior of a coupled platform-turbine system is studied; statistics of tower and rotor loads as well as platform motions are estimated and critical combinations of wind speed and wave height identified. Long-term loads associated with a 50-year return period are estimated using statistical extrapolation based on loads derived from simulations. Inverse reliability procedures that seek appropriate fractile levels for underlying variables consistent with the target load return period are employed; these include use of (1) two-dimensional inverse first-order reliability method where extreme loads, conditional on wind speed and wave height random variables, are selected at median levels and (2) three-dimensional inverse first-order reliability method where variability in the environmental and load random variables is fully represented.

scholarly journals A simplified approach to reliability evaluation of deep rock tunnel deformation using First-Order Reliability Method and Monte Carlo simulations

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Caio Cesar Cardoso da Silva ◽  
Mauro de Vasconcellos Real ◽  
Samir Maghous
Keyword(s):  
Monte Carlo ◽  
Reliability Analysis ◽  
Numerical Models ◽  
Limit State ◽  
Random Variables ◽  
Support Pressure ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method ◽  
Shotcrete Lining

abstract: The Monte Carlo simulation (MCS) and First-Order Reliability Method (FORM) provide a reliability analysis in axisymmetric deep tunnels driven in elastoplastic rocks. The Convergence-Confinement method (CV-CF) and Mohr-Coulomb (M-C) criterion are used to model the mechanical interaction between the shotcrete lining and ground through deterministic parameters and random variables. Numerical models synchronize tunnel analytical models and reliability methods, whereas the limit state functions control the failure probability in both ground plastic zone and shotcrete lining. The results showed that a low dispersion of random variables affects the plastic zone's reliability analysis in unsupported tunnels. Moreover, the support pressure generates a significant reduction in the plastic zone's failure, whereas the increase of shotcrete thickness results in great reduction of the lining collapse probability.

An accuracy analysis method for first-order reliability method

2018 ◽  
Vol 233 (12) ◽  
pp. 4319-4327 ◽  
Author(s):  
Zhenzhong Chen ◽  
Zihao Wu ◽  
Xiaoke Li ◽  
Ge Chen ◽  
Guangfeng Chen ◽  
...  
Keyword(s):  
Structural Reliability ◽  
Nonlinear Problems ◽  
Accuracy Analysis ◽  
Analysis Method ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Level ◽  
Reliability Method ◽  
Value Range ◽  
Crashworthiness Design

The first-order reliability method is widely used for structural reliability analysis; however, its accuracy would become worse for nonlinear problems. This paper proposes the accuracy analysis method of the first-order reliability method, which considers the worst cases when using the first-order reliability method and gives the possible value range of the probability of safety. The accuracy analysis method can evaluate the reliability level of the first-order reliability method when the failure surfaces are nonlinear. The calculation formula for the possible value range of the probability of safety is proposed, and its trend as the dimensions and reliability rise is also discussed in this paper. A numerical example and a honeycomb crashworthiness design are presented to validate the accuracy of the first-order reliability method, and the results show that they are located within the possible value range proposed in this paper.

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