Some Important Issues on First-Order Reliability Analysis With Nonprobabilistic Convex Models

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
C. Jiang ◽  
G. Y. Lu ◽  
X. Han ◽  
R. G. Bi
Keyword(s):  
Reliability Analysis ◽  
Reliability Index ◽  
Probability Model ◽  
Mean Value ◽  
Convex Model ◽  
First Order ◽  
Reliability Method ◽  
Complex Engineering ◽  
Mean Value Method ◽  
Form Type

Compared with the probability model, the convex model approach only requires the bound information on the uncertainty, and can make it possible to conduct the reliability analysis for many complex engineering problems with limited samples. Presently, by introducing the well-established techniques in probability-based reliability analysis, some methods have been successfully developed for convex model reliability. This paper aims to reveal some different phenomena and furthermore some severe paradoxes when extending the widely used first-order reliability method (FORM) into the convex model problems, and whereby provide some useful suggestions and guidelines for convex-model-based reliability analysis. Two FORM-type approximations, namely, the mean-value method and the design-point method, are formulated to efficiently compute the nonprobabilistic reliability index. A comparison is then conducted between these two methods, and some important phenomena different from the traditional FORMs are summarized. The nonprobabilistic reliability index is also extended to treat the system reliability, and some unexpected paradoxes are found through two numerical examples.

On Two First Order Reliability Methods for Computing the Non-Probabilistic Reliability Index

2014 ◽  
Vol 551 ◽  
pp. 648-652
Author(s):  
Xin Zhou Qiao
Keyword(s):  
Reliability Index ◽  
Limit State ◽  
Mean Value ◽  
Point Method ◽  
State Function ◽  
Design Point ◽  
First Order ◽  
Reliability Methods ◽  
The Mean ◽  
Mean Value Method

The two first order reliability methods (FORM) for computing the non-probabilistic reliability index, namely the mean-value method and the design-point method, are investigated. A performance comparison is presented between these two methods. The results show that: (1) the value of the reliability index of the mean-value method depends on the specific form of the limit state function, whereas the value of the reliability index of the design-point one does not;(2) the design-point method should be preferentially used in structural reliability assessment. The conclusions are verified by a numerical example.

scholarly journals Hybrid Approach to the First Order Reliability Method in the Reliability Analysis of a Spatial Structure

2021 ◽  
Vol 11 (2) ◽  
pp. 648
Author(s):  
Agnieszka Dudzik ◽  
Beata Potrzeszcz-Sut
Keyword(s):  
Reliability Analysis ◽  
Reliability Index ◽  
Limit State ◽  
Hybrid Approach ◽  
Reference Method ◽  
Transformation Method ◽  
State Function ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method

The objective of the article involves presenting two approaches to the structure reliability analysis. The primary research method was the First Order Reliability Method (FORM). The Hasofer–Lind reliability index β in conjunction with transformation method in the FORM was adopted as the measure of reliability. The first proposal was combining NUMPRESS software with the non-commercial KRATA program. In this case, the implicit form of the random variables function was created. Limit state function was symbolically given in the standard math notation as a function of the basic random and external variables. The second analysis proposed a hybrid approach enabling the introduction of explicit forms of limit state functions to the reliability program. To create the descriptions of this formula, the neural networks were used and our own original FEM module. The combination of conventional and neural computing can be seen as a hybrid system. The explicit functions were implemented into NUMPRESS software. The values of the reliability index for different descriptions of the mathematical model of the structure were determined. The proposed hybrid approach allowed us to obtain similar results to the results from the reference method.

scholarly journals Reliability analysis of a prestressed bridge beam designed in serviceability limit state as recommended by NBR 6118 and 7188

2020 ◽  
Vol 13 (2) ◽  
pp. 380-397
Author(s):  
P. H. C. DE LYRA ◽  
A. T. BECK ◽  
F. R. STUCCHI
Keyword(s):  
Reliability Analysis ◽  
Reliability Index ◽  
Limit State ◽  
Economic Losses ◽  
Allowable Stress ◽  
State Equations ◽  
First Order ◽  
Serviceability Limit State ◽  
Reliability Method ◽  
Brazilian Standard

Abstract Nowadays it is known that it is important to study the safety of structures to avoid tragic accidents or economic losses. The most widely used method in the world to evaluate the safety of structures is structural reliability. The reliability index of prestressed precast beams of bridges designed using Brazilian standards (NBR6118 and NBR7188) is not known. This work evaluates the annual reliability indexes of a prestressed precast beam bridge at the serviceability limit state (SLS) projected using the Brazilian standard and compares it with results from the literature. The studied bridge has 33.5 meters of span, is simply supported, constituted by five precast concrete beams with U section. The reliability analysis was carried out using two methods for the four limit state equations: First Order Mean Value (FOMV) and First Order Reliability Method (FORM). Sensitivity analyzes were performed to consider both the relative contribution of these variables and the effect of their distributions on the annual reliability indexes for SLS. It was verified that the effect of load trains and the allowable stress significantly reduce the reliability index obtained for Brazilian standard. The service limit state equations are particularly sensitive to load trains, allowable stress and prestress losses, as well as their respective distributions.

Reliability analysis of corroded pipelines: Novel adaptive conjugate first order reliability method

2019 ◽  
Vol 62 ◽  
pp. 103986 ◽  
Author(s):  
Behrooz Keshtegar ◽  
Mohamed El Amine Ben Seghier ◽  
Shun-Peng Zhu ◽  
Rouzbeh Abbassi ◽  
Nguyen-Thoi Trung
Keyword(s):  
Reliability Analysis ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method

scholarly journals A decoupling algorithm with first-order asymptotic integration for reliability-based design optimization

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879333 ◽  
Author(s):  
Zhiliang Huang ◽  
Tongguang Yang ◽  
Fangyi Li
Keyword(s):  
Design Optimization ◽  
Reliability Analysis ◽  
Optimization Problems ◽  
Asymptotic Integration ◽  
Probabilistic Constraints ◽  
Reliability Based Design Optimization ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Based Design ◽  
Reliability Method

Conventional decoupling approaches usually employ first-order reliability method to deal with probabilistic constraints in a reliability-based design optimization problem. In first-order reliability method, constraint functions are transformed into a standard normal space. Extra non-linearity introduced by the non-normal-to-normal transformation may increase the error in reliability analysis and then result in the reliability-based design optimization analysis with insufficient accuracy. In this article, a decoupling approach is proposed to provide an alternative tool for the reliability-based design optimization problems. To improve accuracy, the reliability analysis is performed by first-order asymptotic integration method without any extra non-linearity transformation. To achieve high efficiency, an approximate technique of reliability analysis is given to avoid calculating time-consuming performance function. Two numerical examples and an application of practical laptop structural design are presented to validate the effectiveness of the proposed approach.

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.

System Reliability of Existing Jacket Platform in Malaysian Water (Failure Path and System Reliability Index)

2014 ◽  
Vol 567 ◽  
pp. 307-312 ◽  
Author(s):  
V. John Kurian ◽  
Mohamed Mubarak Abdul Wahab ◽  
T.S. Kheang ◽  
Mohd Shahir Liew
Keyword(s):  
System Reliability ◽  
Reliability Index ◽  
Structural Reliability ◽  
Pushover Analysis ◽  
Probability Of Failure ◽  
Jacket Platform ◽  
First Order ◽  
Failure Path ◽  
Reliability Method ◽  
Two Parameters

The objective of this work is to determine the structural reliability of an existing jacket platform in Malaysia, by determining the system probability of failure and its corresponding reliability index. These two parameters are important indicators for assessing the integrity and reliability of the platform, and will point out whether the platform is suitable for continued operation. In this study, pushover analysis is used to determine possible failure paths of the structure, while First Order Reliability Method (FORM) and Simple Bound Formula are used to determine the failure probability and reliability index. Three failure paths of the platform are established. The reliability index of these paths is found with the highest Reliability Indexto be 18.82 from the 315-degree path, while the system reliability index is 9.23. This illustrates that the platform is robust and the chances of collapse is very small.

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.

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