Investigation of Partial Safety Factor Approach for Flaw Assessment Procedure in Chinese FFS Code

2017 ◽  
Author(s):  
Zhiyuan Han ◽  
Guoshan Xie ◽  
Shanshan Shao ◽  
Zhifeng Li
Keyword(s):  
Safety Factor ◽  
Limit State ◽  
Assessment Procedure ◽  
State Equations ◽  
First Order ◽  
Partial Safety Factor ◽  
Reliability Method ◽  
Failure Assessment ◽  
Derivation Method ◽  
Level 2

Partial safety factor (PSF) is a reliability approach for considering the variance of parameters in flaw assessment procedure in major fitness-for-service (FFS) codes, such as recent API579 and BS7910 codes, but is still not adopted in Chinese FFS code GB/T 19624-2005. This study investigated the derivation method for PSFs based on GB/T 19624 procedure. The limit state equations for PSFs calculation were proposed based on GB/T 19624 level 2 failure assessment diagram (FAD). The distribution of random variables was determined according to China’s domestic features. The first order reliability method (FORM) and second order reliability method (SORM) were employed as reliability analysis methods, and the calculated results were both compared with that simulated using Monte Carlo method. The PSFs at different target reliability levels were established and compared with that in API 579 and BS 7910. The method proposed in this study provides a basis for introducing PSF approach into Chinese FFS code.

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.

Partial Safety Factor (PSF) Approach in Japanese FFS Code, HPIS Z101 Level 2

2006 ◽  
Author(s):  
Ikuo Kojima ◽  
Shinji Konosu
Keyword(s):  
Safety Factor ◽  
Material Properties ◽  
Moment Method ◽  
Method Comparison ◽  
First Order ◽  
Partial Safety Factor ◽  
First Order Second Moment ◽  
Fracture Assessment ◽  
Second Moment Method ◽  
Level 2

One of the features of HPIS Z101 Level2 method, which is to be published as a Japanese Fitness-For-Service (FFS) code for pressure equipment, is to use plural FADs (Fracture Assessment Diagrams) dependent on the materials. Regarding the FFS assessment methods for a crack-like flaw, Partial Safety Factor (PSF) is becoming a major approach in considering the effect of the variance of parameters for the assessment, such as applied load, material properties and detected dimensions of the flaws concerned. To apply this approach to the HPIS code, different PFSs for FADs dependent on materials should be prepared. PSFs with various conditions are calculated for the HPIS code, by the AFOSM (Advanced First-Order Second Moment) method. Comparison with PSFs in other preceding FFS codes, such as API 579-2000 and BS 7910-2005, is being conducted.

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.

Second-Order Reliability Method With First-Order Efficiency

2010 ◽  
Author(s):  
Xiaoping Du ◽  
Junfu Zhang
Keyword(s):  
Limit State ◽  
Saddlepoint Approximation ◽  
Quadratic Function ◽  
Second Order ◽  
State Function ◽  
Limit State Function ◽  
First Order ◽  
Reliability Method ◽  
Order Of Magnitude ◽  
Univariate Function

The widely used First Order Reliability Method (FORM) is efficient, but may not be accurate for nonlinear limit-state functions. The Second Order Reliability Method (SORM) is more accurate but less efficient. To maintain both high accuracy and efficiency, we propose a new second order reliability analysis method with first order efficiency. The method first performs the FORM and identifies the Most Probable Point (MPP). Then the associated limit-state function is decomposed into additive univariate functions at the MPP. Each univariate function is further approximated as a quadratic function, which is created with the gradient information at the MPP and one more point near the MPP. The cumulant generating function of the approximated limit-state function is then available so that saddlepoint approximation can be easily applied for computing the probability of failure. The accuracy of the new method is comparable to that of the SORM, and its efficiency is in the same order of magnitude as the 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.

In-Plane Creep Stability Design of Concrete Filled Steel Tubular Arches Using Inverse Reliability Method

2013 ◽  
Vol 351-352 ◽  
pp. 1601-1604
Author(s):  
Wei Jiang ◽  
Da Gang Lu
Keyword(s):  
Limit State ◽  
Time Dependent ◽  
Safety Factors ◽  
State Equations ◽  
Stability Range ◽  
Good Efficiency ◽  
Reliability Indices ◽  
Inverse Form ◽  
Dependent Behavior ◽  
Reliability Method

An inverse first order reliability method (FORM) is presented to solve the safety factors for the in-plane creep stability of concrete filled steel tubular (CFST) arches. In the inverse analysis, the safety factors with or without considering the time-dependent behavior of concrete are introduced into limit state equations for the in-plane stability design of CFST arches. For different target reliability indices and steel ratios, the time-independent and time-dependent safety factors are solved. The results show that the inverse FORM is of good efficiency and applicability. The target reliability indices have little effect on the safety factors for the creep stability of CFST arches. The effects of steel ratios are significant which should be considered in design. For the commonly used steel ratios of CFST arches, the in-plane safety factors for creep stability range from 1.17 to 1.43.

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.

System Reliability Analysis With Autocorrelated Kriging Predictions

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Hao Wu ◽  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Limit State ◽  
Surrogate Models ◽  
First Order ◽  
Highly Nonlinear ◽  
Reliability Method ◽  
Reliability Methods ◽  
Computationally Intensive ◽  
Improved Accuracy ◽  
State Functions

Abstract When limit-state functions are highly nonlinear, traditional reliability methods, such as the first-order and second-order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient sample size is used but is computationally intensive. This research proposes a new system reliability method that combines MCS and the Kriging method with improved accuracy and efficiency. Accurate surrogate models are created for limit-state functions with minimal variance in the estimate of the system reliability, thereby producing high accuracy for the system reliability prediction. Instead of employing global optimization, this method uses MCS samples from which training points for the surrogate models are selected. By considering the autocorrelation of a surrogate model, this method captures the more accurate contribution of each MCS sample to the uncertainty in the estimate of the serial system reliability and therefore chooses training points efficiently. Good accuracy and efficiency are demonstrated by four examples.

Reliability-Based Derivation of Partial Safety Factor for Structural Integrity Assessment

2008 ◽  
Author(s):  
Shuo Pan ◽  
Jianping Zhao
Keyword(s):  
Safety Factor ◽  
Fracture Mode ◽  
Structural Integrity ◽  
Research Work ◽  
Random Variables ◽  
Assessment Procedure ◽  
Safety Factors ◽  
Integrity Assessment ◽  
Partial Safety Factor ◽  
Partial Safety Factors

When there are uncertainties in the input random variables, or scatter in the material properties, probabilistic assessment is a useful tool for decision making in the field of safety analysis. The partial safety factor (PSF) method was aimed on ensuring that the failure probability did not exceed a target value. In order to be conservative the input value for each random variable during the assessment procedure should be multiplied by the partial safety factors. So it is essentially a deterministic assessment using conservative values of the input random variables and a relatively simple and independent method of assessing failure probabilities using R6 failure assessment diagram. The application of partial safety factors is an important breakthrough of assessment in structures containing defects. In recent years, sets of PSFs for load, defect size, fracture toughness and yield stress had been given in two standards, BS7910 and API579. However, the recommended PSFs in both standards were larger than the original PSFs in PD6493 which was replaced by BS7910. It is therefore a new method of calculating PSFs should be found to prove which is more appropriate and convenient for engineering application. In the case of the partial safety factor method target reliabilities in the range from 0.001 to 0.00001 were considered and new series of PSFs were derived from the results of reliability analysis for the linear elastic fracture mode and elastic-plastic fracture mode. After comparing with the PSFs in BS7910 and API 579, it is concluded that the partial safety factors were generally conservative compared to our research work.

Reliability and Sensitivity Analysis of Laminate Using Different Methods

2014 ◽  
Vol 945-949 ◽  
pp. 1159-1162
Author(s):  
Wei Tao Zhao ◽  
Xiao Li ◽  
Feng Guo
Keyword(s):  
Good Accuracy ◽  
Limit State ◽  
Nonlinear Function ◽  
State Function ◽  
Laminate Structure ◽  
First Order Reliability Method ◽  
First Order ◽  
Fiber Properties ◽  
Surface Method ◽  
Reliability Method

Reliability of laminate structure is deeply influenced by uncertainties such as fiber properties, loads and design sizes. It is very difficult to evaluate the reliability and sensitivity of laminate structure because that laminate structure is anisotropic and the limit state function (LSF) is a high nonlinear function. In this paper, reliability and sensitivity are evaluated by using first order reliability method (FORM), response surface method (RSM) and Monte Carlo simulation (MCS). The study aims to find a numerical method to evaluate the reliability and sensitivity of laminate structures efficiently and accurately. An example of laminate with a large number of variables is analyzed. The results obtained by using different methods are compared in terms of efficiency and accuracy. It is shown that FORM is not accuracy, and RSM has a very good accuracy and efficient in terms of reliability, but the accuracy of sensitivity obtained by using RSM is not good enough.

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