Hybrid control variates-based simulation method for structural reliability analysis of some problems with low failure probability

2018 ◽  
Vol 60 ◽  
pp. 220-234 ◽  
Author(s):  
Mohsen Rashki
Keyword(s):  
Reliability Analysis ◽  
Failure Probability ◽  
Structural Reliability ◽  
Hybrid Control ◽  
Simulation Method ◽  
Control Variates ◽  
Structural Reliability Analysis

Structural Reliability Analysis Using Fourier Orthogonal Neural Network Method

2013 ◽  
Vol 838-841 ◽  
pp. 360-363 ◽  
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Failure Probability ◽  
Structural Reliability ◽  
Random Variables ◽  
Structural Reliability Analysis ◽  
Surface Method ◽  
Structural Responses

In order to predict the failure probability of a complicated structure, the structural responses usually need to be predicted by a numerical procedure, such as FEM method. The response surface method could be used to reduce the computational effort required for reliability analysis. However the conventional response surface method is still time consuming when the number of random variables is large. In this paper, a Fourier orthogonal neural network (FONN)-based response surface method is proposed. In this method, the relationship between the random variables and structural responses is established using FONN models. Then the FONN model is connected to the first order and second moment method (FORM) to predict the failure probability. Numerical example result shows that the proposed approach is efficient and accurate, and is applicable to structural reliability analysis.

RCA-PCK: A new structural reliability analysis method based on PC-Kriging and radial centralized adaptive sampling strategy

2020 ◽  
pp. 095440622095771
Author(s):  
Zhenliang Yu ◽  
Zhili Sun ◽  
Runan Cao ◽  
Jian Wang ◽  
Yutao Yan
Keyword(s):  
Reliability Analysis ◽  
Failure Probability ◽  
Adaptive Sampling ◽  
Structural Reliability ◽  
Sampling Strategy ◽  
Kriging Model ◽  
Strong Nonlinearity ◽  
Analysis Method ◽  
Structural Reliability Analysis ◽  
Training Samples

To improve the efficiency and accuracy of reliability assessment for structures with small failure probability and time-consuming simulation, a new structural reliability analysis method (RCA-PCK) is proposed, which combines PC-Kriging model and radial centralized adaptive sampling strategy. Firstly, the PC-Kriging model is constructed by improving the basis function of Kriging model with sparse polynomials. Then, the sampling region which contributes a great impact on the failure probability is constructed by combining the radial concentration and important sampling technology. Subsequently, the k-means++ clustering technology and learning function LIF are adopted to select new training samples from each subdomains in each iteration. To avoid the sampling distance in one subdomain or the distance between the new training samples in two subdomains being too small, we construct a screening mechanism to ensure that the selected new training samples are evenly distributed in the limit state. In addition, a new convergence criterion is derived based on the relative error estimation of failure probability. Four benchmark examples are given to illustrate the convergence process, accuracy and stability of the proposed method. Finally, the transmission error reliability analysis of thermal-elastic coupled gears is carried out to prove the applicability of the proposed method RCA-PCK to the structures with strong nonlinearity and time-consuming simulation.

KNN Classification Based MCS Method of Structural Reliability Analysis

2014 ◽  
Vol 638-640 ◽  
pp. 136-139 ◽  
Author(s):  
Ying Zhao ◽  
Guo Shao Su ◽  
Liu Bin Yan
Keyword(s):  
Reliability Analysis ◽  
Failure Probability ◽  
Structural Reliability ◽  
Simulation Method ◽  
Superior Performance ◽  
Performance Function ◽  
Training Samples ◽  
Study Results ◽  
Knn Classification ◽  
Mcs Method

A KNN Classification Based MCS (Monte Carlo Simulation Method) is proposed for the reliability analysis which hindered by the implicit nature of the performance function. In the method, Markov chain is adopted to simulate a small amount of training samples, KNN classification is used to generate surrogate model of performance function, MCS is used to estimate the failure probability. An iterative algorithm is presented to improve surrogate precision dynamically in the region contributing to the failure probability significantly. The study results demonstrate that the proposed method has superior performance to the traditional response surface method.

A New Response Surface Method for Structural Reliability Analysis

2013 ◽  
Vol 712-715 ◽  
pp. 1506-1509 ◽  
Author(s):  
Guang Bo Li ◽  
Guang Wei Meng ◽  
Feng Li ◽  
Li Ming Zhou
Keyword(s):  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Failure Probability ◽  
Structural Reliability ◽  
Uniform Design ◽  
State Function ◽  
Structural Reliability Analysis ◽  
Surface Method ◽  
Reliability Method

The response surface method is adopted to analyze the structural reliability. This paper presents a new response surface method with the uniform design method to predict the failure probability of structures. It is the response surface method based on Fourier orthogonal basis function (RSM-Fourier). To reduce computational costs in structural reliability analysis, approximate Fourier response surface functions for reliability assessment have been suggested. The method involves the selection of training datasets for establishing a model by the uniform design points, the approximation of the limit state function by the trained model and the estimation of the failure probability using first-order reliability method (FORM). The proposed method is applied to examples, compared with other methods to demonstrate its effectiveness.

Structural Reliability Analysis Using Legendre Orthogonal Neural Network Method

2014 ◽  
Vol 501-504 ◽  
pp. 1077-1080
Author(s):  
Li Rong Sha ◽  
Yong Chun Shi
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Failure Probability ◽  
Structural Reliability ◽  
Random Variables ◽  
Structural Reliability Analysis ◽  
Surface Method ◽  
Structural Responses

In order to predict the failure probability of a complicated structure, the structural responses usually need to be estimated by a numerical analysis such as finite element method. The response surface method could be used to reduce the computational effort required for reliability analysis when the performance functions are implicit. However the conventional response surface method is time-consuming or cumbersome if the number of random variables is large. This paper presents a Legendre orthogonal neural network (LONN)-based response surface method to predict the reliability of a structure. In this method, the relationship between the random variables and structural responses is established by a LONN model. Then the LONN model is connected to a reliability method, i.e. first-order reliability methods (FORM) to predict the failure probability of the structure. Numerical example has shown that the proposed approach is applicable to structural reliability analysis involving implicit performance functions.

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