Limit state functions and parameter-depending uncertainty described by sets of probability measures

2014 ◽  
pp. 423-430
Author(s):  
Thomas Fetz
Keyword(s):  
Limit State ◽  
Probability Measures ◽  
State Functions

A New Methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing

2011 ◽  
Vol 274 ◽  
pp. 101-111 ◽  
Author(s):  
Norelislam Elhami ◽  
Rachid Ellaia ◽  
Mhamed Itmi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Numerical Models ◽  
Limit State ◽  
Particle Swarm ◽  
Geometrical Parameters ◽  
Rectangular Plates ◽  
Swarm Optimization ◽  
Reliability Methods ◽  
State Functions

This paper presents a new methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing. The reliability analysis procedure couple traditional and modified first and second order reliability methods, in rectangular plates modelled by an Assumed Modes approach. Both reliability methods are applicable to the implicit limit state functions through numerical models, like those based on the Assumed Mode Method. For traditional reliability approaches, the algorithms FORM and SORM use a Newton-Raphson procedure for estimate design point. In modified approaches, the algorithms are based on heuristic optimization methods such as Particle Swarm Optimization and Simulated Annealing Optimization. Numerical applications in static, dynamic and stability problems are used to illustrate the applicability and effectiveness of proposed methodology. These examples consist in a rectangular plates subjected to in-plane external loads, material and geometrical parameters which are considered as random variables. The results show that the predicted reliability levels are accurate to evaluate simultaneously various implicit limit state functions with respect to static, dynamic and stability criterions.

Circular Arc Gear Reliability Study Based on Modified FOSM Method

2013 ◽  
Vol 760-762 ◽  
pp. 2216-2219
Author(s):  
Zhong Li ◽  
Bo Yu Cheng
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Limit State ◽  
Point Method ◽  
Reliability Study ◽  
Circular Arc ◽  
Gear Case ◽  
Improve Reliability ◽  
State Functions

As different limit state functions are used to analyze reliability, there is a great distinctness among the calculated results. In this paper an improved LOSM method is proposed, namely, checking point method. The circular arc gear case is employed to demonstrate this method. In contrast to the results of Monte Carlo simulation, this method can greatly improve reliability calculations precision.

Probabilistic Assessment of a 70-Year Old Pipeline Subject to Seismic Deformation

2006 ◽  
Author(s):  
Robert W. Warke ◽  
James D. Hart ◽  
Ben H. Thacker
Keyword(s):  
Seismic Vulnerability ◽  
Limit State ◽  
Probabilistic Assessment ◽  
Weld Joints ◽  
Natural Gas Pipeline ◽  
Flaw Sizing ◽  
Probability Estimates ◽  
Seismic Deformation ◽  
State Functions

This paper presents an assessment case study on several segments of buried natural gas pipeline constructed in 1936 with ‘bell-bell-chill ring’ (BBCR) style girth weld joints, and currently operating in a seismically active region of North America. Seismic vulnerability was evaluated in terms of girth weld fracture and plastic collapse probabilities for specified hazards of varying severity and likelihood. Monte Carlo simulations performed in NESSUS® provided failure probability estimates from distributed inputs based on PIPLIN deformation analyses, nondestructive and destructive flaw sizing, residual stress measurements, weld metal tensile and CTOD tests, and limit state functions based on published stress intensity and collapse solutions.

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 Assessment Based on the Concept of Failure Surface Frontier

2005 ◽  
Author(s):  
Songqing Shan ◽  
G. Gary Wang
Keyword(s):  
High Efficiency ◽  
Limit State ◽  
Reliability Assessment ◽  
Failure Surface ◽  
Test Results ◽  
Limit States ◽  
Failure Region ◽  
Novel Concept ◽  
Quadratic Approximations ◽  
State Functions

This work proposes a novel concept of failure surface frontier (FSF), which is a hyper-surface consisting of the set of the non-dominated failure points on the limit states of a given failure region. FSF better represents the limit state functions for reliability assessment than conventional linear or quadratic approximations on the most probable point (MPP). Assumptions, definitions, and benefits of FSF are discussed first in detail. Then, a discriminative sampling based algorithm was proposed to identify FSF, from which reliability is assessed. Test results on well known problems show that reliability can be accurately estimated with high efficiency. The algorithm is also effective for problems of multiple failure regions, multiple most probable points (MPP), or failure regions of extremely small probability.

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