Reliability Analysis Based on Efficient Simulation

2011 ◽  
Vol 141 ◽  
pp. 594-600
Author(s):  
Xian Sheng Gong ◽  
Gan Qing Zhang
Keyword(s):  
Importance Sampling ◽  
Failure Probability ◽  
Structural Reliability ◽  
Probability Density Functions ◽  
Shield Tunneling ◽  
Efficient Simulation ◽  
Practical Engineering ◽  
Tunneling Machine ◽  
Continuous Product ◽  
Failure Probabilities

In the practical engineering, to solve the small failure probabilities with correlated high-dimensional variables, the subset simulation (SS) is combined together with the Monte Carlo and importance sampling (IS) method. The samples from the probability density functions (PDF) of the importance sampling are used to construct the intermediate failure events, by which the small failure probabilities are turned into a Markov chain (MC), which is a continuous product made of a series large failure probability or conditional failure probability (CFP) which is easily answered, on which the structural reliability can be efficiently simulated by directly obtaining the samples with correlated ones. Finally, the 3 planet carriers of 3 grade planetary reducers in shield tunneling machine(STM) are as examples to check the algorithm above, the results show that the SS of the IS with correlated variables can highly simulate failure probability.

Structural reliability assessment based on low-discrepancy adaptive importance sampling and artificial neural network

2016 ◽  
Vol 231 (3) ◽  
pp. 497-509 ◽  
Author(s):  
Hailong Zhao ◽  
Zhufeng Yue ◽  
Yongshou Liu ◽  
Wei Liu ◽  
Zongzhan Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Markov Chain ◽  
Importance Sampling ◽  
Failure Probability ◽  
Structural Reliability ◽  
Chain Process ◽  
Performance Function ◽  
Function Calls ◽  
Artificial Neural

In the field of structural reliability, the estimation of failure probability often requires large numbers of time-consuming performance function calls. It is a great challenge to keep the number of function calls to a minimum extent. The aim of this paper is to propose an approach to assess the structural reliability in an efficient way. The proposed method could be viewed as a hybrid reliability method which combines the advantages of adaptive importance sampling, low-discrepancy sampling and artificial neural network. In the proposed method, artificial neural network is introduced to alleviate the computational burden of deterministic and boring engineering analysis, and its introduction guarantees the computational efficiency of the proposed method. While the Markov chain process is adopted to generate the experimental samples which are used to construct the artificial neural network, the introduction of Markov chain process guarantees the adaptivity of the proposed method and makes the proposed method applicable for various reliability problems. The proposed method is shown to be very efficient as the estimated failure probability is very accurate and only a small number of calls to the actual performance function are needed. The effectiveness and engineering applicability of the proposed method are demonstrated by several test examples.

FORWARD APPORTIONMENT OF CENSORED COUNTS FOR DISCRETE NONPARAMETRIC MAXIMUM LIKELIHOOD ESTIMATION OF FAILURE PROBABILITIES

2009 ◽  
Vol 16 (03) ◽  
pp. 213-234
Author(s):  
MINNIE H. PATEL ◽  
H.-S. JACOB TSAO
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Closed Form ◽  
Failure Probability ◽  
Likelihood Estimation ◽  
Lifetime Distribution ◽  
Nonparametric Maximum Likelihood ◽  
Kaplan Meier ◽  
Probability Estimates ◽  
Failure Probabilities

Empirical cumulative lifetime distribution function is often required for selecting lifetime distribution. When some test items are censored from testing before failure, this function needs to be estimated, often via the approach of discrete nonparametric maximum likelihood estimation (DN-MLE). In this approach, this empirical function is expressed as a discrete set of failure-probability estimates. Kaplan and Meier used this approach and obtained a product-limit estimate for the survivor function, in terms exclusively of the hazard probabilities, and the equivalent failure-probability estimates. They cleverly expressed the likelihood function as the product of terms each of which involves only one hazard probability ease of derivation, but the estimates for failure probabilities are complex functions of hazard probabilities. Because there are no closed-form expressions for the failure probabilities, the estimates have been calculated numerically. More importantly, it has been difficult to study the behavior of the failure probability estimates, e.g., the standard errors, particularly when the sample size is not very large. This paper first derives closed-form expressions for the failure probabilities. For the special case of no censoring, the DN-MLE estimates for the failure probabilities are in closed forms and have an obvious, intuitive interpretation. However, the Kaplan–Meier failure-probability estimates for cases involving censored data defy interpretation and intuition. This paper then develops a simple algorithm that not only produces these estimates but also provides a clear, intuitive justification for the estimates. We prove that the algorithm indeed produces the DN-MLE estimates and demonstrate numerically their equivalence to the Kaplan–Meier-based estimates. We also provide an alternative algorithm.

Reliability Analysis of Reinforced Concrete Structural during Construction

2011 ◽  
Vol 71-78 ◽  
pp. 310-314
Author(s):  
Jun Zhao
Keyword(s):  
Reinforced Concrete ◽  
Stochastic Analysis ◽  
Reliability Index ◽  
Structural Reliability ◽  
Reinforced Concrete Structures ◽  
Time Varying ◽  
Calculation Algorithm ◽  
Random Field Theory ◽  
Reliability Calculation ◽  
Practical Engineering

According to random field theory, combined with the construction of the characteristics of reinforced concrete structures, based on the geometric significance of the reliability index, the optimization algorithm of the reliability was established, and the reliability calculation algorithm of reinforced concrete structural during construction is proposed based on stochastic finite element method. Based on a stochastic analysis of the practical engineering, the time-varying laws of the reinforced concrete structural reliability index during construction are concluded.

Evaluation of the Effect of Yield to Tensile (Y/T) Ratio on the Structural Integrity of Offshore Pipeline by Limit State Design Approach

2006 ◽  
Author(s):  
Gianluca Mannucci ◽  
Giuliano Malatesta ◽  
Giuseppe Demofonti ◽  
Marco Tivelli ◽  
Hector Quintanilla ◽  
...  
Keyword(s):  
Structural Reliability ◽  
Failure Modes ◽  
Structural Integrity ◽  
Limit State ◽  
Minor Effect ◽  
Offshore Pipelines ◽  
Limit State Design ◽  
A Minor ◽  
Probabilistic Failure Analysis ◽  
Failure Probabilities

Nowadays specifications require strict Yield to Tensile ratio limitation, nevertheless a fully accepted engineering assessment of its influence on pipeline integrity is still lacking. Probabilistic analysis based on structural reliability approach (Limit State Design, LSD) aimed at quantifying the yield to tensile strength ratio (Y/T) influence on failure probabilities of offshore pipelines was made. In particular, Tenaris seamless pipe data were used as input for the probabilistic failure analysis. The LSD approach has been applied to two actual deepwater design cases that have been on purpose selected, and the most relevant failure modes have been considered. Main result of the work is that the quantitative effect of the Y/T ratio on failure probabilities of a deepwater pipeline resulted not so big as expected; it has a minor effect, especially when Y only governs failure modes.

Sign in / Sign up

Export Citation Format

Share Document