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.

A Random Field Method for Time-Dependent Reliability Analysis With Random and Interval Variables

2016 ◽  
Author(s):  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Random Field ◽  
Limit State ◽  
Gaussian Random Field ◽  
Field Method ◽  
Time Dependent ◽  
State Function ◽  
Good Efficiency ◽  
First Order ◽  
Interval Variables ◽  
Reliability Method

In many engineering applications, both random and interval variables exist. Some of the random variables may also vary over time. As a result, the reliability of a component not only decreases with time but also resides in an interval. Evaluating the time-dependent reliability bounds is a challenging task because of the intensive computational demand. This research develops a method that treats a time-dependent random response as a random field with respect to both intervals and time. Consequently, random field methodologies can be used to estimate the worse-case time-dependent reliability. The method employs the first-order reliability method, which results in a Gaussian random field for the response with respect to intervals and time. The Kriging method and Monte Carlo simulation are then used to estimate the worse-case reliability without calling the original limit-state function. Good efficiency and accuracy are demonstrated through examples.

scholarly journals Computer-aided SPT-based reliability model for probability of liquefaction using hybrid PSO and GA

2020 ◽  
Vol 7 (1) ◽  
pp. 107-127 ◽  
Author(s):  
Maral Goharzay ◽  
Ali Noorzad ◽  
Ahmadreza Mahboubi Ardakani ◽  
Mostafa Jalal
Keyword(s):  
Engineering Design ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Probabilistic Method ◽  
Limit State ◽  
Soil Liquefaction ◽  
Probability Models ◽  
Reliability Indices ◽  
Hybrid Particle Swarm Optimization ◽  
Reliability Method

Abstract In this paper, an approach for soil liquefaction evaluation using probabilistic method based on the world-wide SPT databases has been presented. In this respect, the parameters’ uncertainties for liquefaction probability have been taken into account. A calibrated mapping function is developed using Bayes’ theorem in order to capture the failure probabilities in the absence of the knowledge of parameter uncertainty. The probability models provide a simple, but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction triggering thresholds. Within an extended framework of the first-order reliability method considering uncertainties, the reliability indices are determined through a well-performed meta-heuristic optimization algorithm called hybrid particle swarm optimization and genetic algorithm to find the most accurate liquefaction probabilities. Finally, the effects of the level of parameter uncertainty on liquefaction probability, as well as the quantification of the limit state model uncertainty in order to incorporate the correct model uncertainty, are investigated in the context of probabilistic reliability analysis. The results gained from the presented probabilistic model and the available models in the literature show the fact that the developed approach can be a robust tool for engineering design and analysis of liquefaction as a natural disaster.

Assessment of time-dependent reliability of reinforced concrete columns with uncertain load eccentricity

2000 ◽  
Vol 27 (3) ◽  
pp. 389-399
Author(s):  
H P Hong ◽  
W Zhou
Keyword(s):  
Reinforced Concrete ◽  
Reliability Analysis ◽  
Axial Load ◽  
Bending Moment ◽  
Time Dependent ◽  
Sensitivity Analyses ◽  
Live Load ◽  
Rc Columns ◽  
Reliability Indices ◽  
Reliability Method

An approach for the time-dependent reliability analysis of reinforced concrete (RC) columns considering the correlation between the axial load and the bending moment or the uncertainty in the load eccentricity is presented. The approach recursively uses the efficient first-order reliability method for the time-dependent reliability analysis. The proposed approach is more efficient than the ones used in the literature for the reliability analysis of RC columns. The proposed approach is used to carry out sensitivity analyses of the reliability of short RC columns to the time-dependent live load effects and to the correlation between the axial load and the bending moment. Results of the analyses suggest that the reliability of RC columns can be sensitive to the correlation between the axial load and the bending moment due to live load. The differences between the reliability indices obtained by considering the live load modeled as a pulse process and as an extreme variate can be large.Key words: reliability, load, time-dependent, time-independent, uncertainty, correlation, concrete, reinforcement, column.

Application of Partial Safety Factors for Fitness-for-Service Assessment of Pressure Equipment With Local Metal Loss

2017 ◽  
Author(s):  
Takuyo Kaida ◽  
Shinsuke Sakai
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Probability Of Failure ◽  
Pressure Vessels ◽  
Metal Loss ◽  
State Function ◽  
Safety Factors ◽  
Loss Assessment ◽  
Reliability Method ◽  
Partial Safety Factors

Reliability analysis considering data uncertainties can be used to make a rational decision as to whether to run or repair a pressure equipment that contains a flaw. Especially, partial safety factors (PSF) method is one of the most useful reliability analysis procedure and considered in a Level 3 assessment of a crack-like flaw in API 579-1/ASME FFS-1:2016. High Pressure Institute of Japan (HPI) formed a committee to develop a HPI FFS standard including PSF method. To apply the PSF method effectively, the safety factors for each dominant variable should be prepared before the assessment. In this paper, PSF for metal loss assessment of typical pressure vessels are derived based on first order reliability method (FORM). First, a limit state function and stochastic properties of random variables are defined. The properties of a typical pressure vessel are based on actual data of towers in petroleum and petrochemical plants. Second, probability of failure in several cases are studied by Hasofer-Lind method. Finally, PSF’s in each target probability of failure are proposed. HPI published a new technical report, HPIS Z 109 TR:2016, that provide metal loss assessment procedures based on FORM and the proposed PSF’s described in this paper.

Time-Dependent Reliability Analysis by a Sampling Approach to Extreme Values of Stochastic Processes

2012 ◽  
Author(s):  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Stochastic Processes ◽  
Reliability Analysis ◽  
Extreme Values ◽  
Limit State ◽  
Time Dependent ◽  
State Function ◽  
Limit State Function ◽  
Reliability Method ◽  
Time Invariant ◽  
State Functions

Maintaining high accuracy and efficiency is a challenging issue in time-dependent reliability analysis. In this work, an accurate and efficient method is proposed for limit-state functions with the following features: The limit-state function is implicit with respect to time, and its input contains stochastic processes; the stochastic processes include only general strength and stress variables, or the limit-state function is monotonic to these stochastic processes. The new method employs random sampling approaches to estimate the distributions of the extreme values of the stochastic processes. The extreme values are then used to replace the corresponding stochastic processes, and consequently the time-dependent reliability analysis is converted into its time-invariant counterpart. The commonly used time-invariant reliability method, the First Order Reliability Method, is then applied for the time-variant reliability analysis. The results show that the proposed method significantly improves the accuracy and efficiency of time-dependent reliability analysis.

Time-Dependent System Reliability Analysis With Second-Order Reliability Method

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Hao Wu ◽  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Second Order ◽  
Time Dependent ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems ◽  
State Functions ◽  
Time Dependent System

Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failures. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method using the envelope method and second-order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the second-order component reliability method with an improve envelope approach, which produces a component reliability index. The covariance between component responses is estimated with the first-order approximations, which are available from the second-order approximations of the component reliability analysis. Then, the joint distribution of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.

Time-Dependent System Reliability Analysis With Second Order Reliability Method

2020 ◽  
Author(s):  
Hao Wu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Second Order ◽  
Time Dependent ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems ◽  
State Functions ◽  
Time Dependent System

Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failure. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method that uses the envelop method and second order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the existing second order component reliability method, which produces component reliability indexes. The covariance between components responses are estimated with the first order approximations, which are available from the second order approximations of the component reliability analysis. Then the joint probability of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.

Time-Dependent Reliability Analysis Using the Total Probability Theorem

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Zissimos P. Mourelatos ◽  
Monica Majcher ◽  
Vijitashwa Pandey ◽  
Igor Baseski
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Random Processes ◽  
Time Dependent ◽  
Normal Space ◽  
Total Probability ◽  
Conditional Probabilities ◽  
Reliability Method ◽  
Standard Normal ◽  
Total Probability Theorem

A new reliability analysis method is proposed for time-dependent problems with explicit in time limit-state functions of input random variables and input random processes using the total probability theorem and the concept of composite limit state. The input random processes are assumed Gaussian. They are expressed in terms of standard normal variables using a spectral decomposition method. The total probability theorem is employed to calculate the time-dependent probability of failure using time-dependent conditional probabilities which are computed accurately and efficiently in the standard normal space using the first-order reliability method (FORM) and a composite limit state of linear instantaneous limit states. If the dimensionality of the total probability theorem integral is small, we can easily calculate it using Gauss quadrature numerical integration. Otherwise, simple Monte Carlo simulation (MCS) or adaptive importance sampling are used based on a Kriging metamodel of the conditional probabilities. An example from the literature on the design of a hydrokinetic turbine blade under time-dependent river flow load demonstrates all developments.

Reliability Analysis for Multidisciplinary Systems Involving Stationary Stochastic Processes

2015 ◽  
Author(s):  
Zhifu Zhu ◽  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Stochastic Processes ◽  
Reliability Analysis ◽  
Limit State ◽  
Time Dependent ◽  
State Function ◽  
Limit State Function ◽  
Current Time ◽  
Reliability Method ◽  
Stationary Stochastic Processes ◽  
Multidisciplinary System

The response of a component in a multidisciplinary system is affected by not only the discipline to which it belongs, but also by other disciplines of the system. If any components are subject to time-dependent uncertainties, responses of all the components and the system are also time dependent. Thus, time-dependent multidisciplinary reliability analysis is required. To extend the current time-dependent reliability analysis for a single component, this work develops a time-dependent multidisciplinary reliability method for components in a multidisciplinary system under stationary stochastic processes. The method modifies the First and Second Order Reliability Methods (FORM and SORM) so that the Multidisciplinary Analysis (MDA) is incorporated while approximating the limit-state function of the component under consideration. Then Monte Carlo simulation is used to calculate the reliability without calling the original limit-state function. Two examples are used to demonstrate and evaluate the proposed method.

Prediction of Fracture Failure of Steel Pipes With Sharp Corrosion Pits Using Time-Dependent Reliability Method With Lognormal Process

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Guoyang Fu ◽  
Wei Yang ◽  
Wenni Deng ◽  
Chun-Qing Li ◽  
Sujeeva Setunge
Keyword(s):  
Fracture Toughness ◽  
Limit State ◽  
Probability Of Failure ◽  
Considerable Effect ◽  
Time Dependent ◽  
Pipe Failure ◽  
Load Effect ◽  
Steel Pipes ◽  
Reliability Method ◽  
Corrosion Pits

This paper presents a reliability-based methodology for assessing fracture failures of steel pipes with sharp corrosion pits. Based on newly developed models of elastic fracture toughness, the simple criterion of stress intensity factor (SIF) is used to establish the limit state functions for pipes with sharp corrosion pits in the longitudinal and circumferential directions. A stochastic model of load effect is developed and a time-dependent reliability method based on first passage probability for nonstationary lognormal processes is employed to quantify the probability of failure and predict the remaining service life. After applying the methodology to a case study, sensitivity analysis is carried out to identify the most influential variables on the probability of failure. It is found in the paper that the correlation coefficient has a considerable effect on probability of failure of steel pipes with sharp corrosion pits and that the larger the mode I fracture toughness is, the smaller the probability of pipe failure is.

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