scholarly journals Regional importance effect analysis of the input variables on failure probability and its state dependent parameter estimation

2013 ◽  
Vol 66 (10) ◽  
pp. 2075-2091 ◽  
Author(s):  
Luyi Li ◽  
Zhenzhou Lu
Keyword(s):  
Parameter Estimation ◽  
Failure Probability ◽  
Effect Analysis ◽  
State Dependent ◽  
Dependent Parameter ◽  
Input Variables

Regional importance measures based on failure probability in the presence of epistemic and aleatory uncertainties

2013 ◽  
Vol 228 (6) ◽  
pp. 1029-1047 ◽  
Author(s):  
Lei Cheng ◽  
Zhenzhou Lu ◽  
Luyi Li
Keyword(s):  
Failure Probability ◽  
Computational Cost ◽  
Variance Decomposition ◽  
The State ◽  
Structural Systems ◽  
Conditional Mean ◽  
First Order ◽  
State Dependent ◽  
Main Effect ◽  
Dependent Parameter

For the structural systems with both epistemic and aleatory uncertainties, in order to analyze the effects of different regions of epistemic parameters on failure probability, two regional importance measures (RIMs) are firstly proposed, i.e. contribution to mean of failure probability (CMFP) and contribution to variance of failure probability (CVFP), and their properties are analyzed and verified. Then, to analyze the effect of different regions of the epistemic parameters on their corresponding first-order variance (i.e. main effect) in the Sobol’s variance decomposition, another RIM is proposed which is named as contribution to variance of conditional mean of failure probability (CVCFP). The proposed CVCFP is then extended to define another RIM named as contribution to mean of conditional mean of failure probability, i.e. CMCFP, to measure the contribution of regions of epistemic parameters to mean of conditional mean of failure probability. For the problem that the computational cost for calculating the conditional mean of failure probability may be too large to be accepted, the state dependent parameter (SDP) method is introduced to estimate CVCFP and CMCFP. Several examples are used to demonstrate the effectiveness of the proposed RIMs and the efficiency and accuracy of the SDP-based method are also demonstrated by the examples.

A global sensitivity analysis method based on ANOVA with convex set model and its state-dependent parameter solution

2018 ◽  
Vol 233 (11) ◽  
pp. 4039-4051
Author(s):  
Qing Guo ◽  
Yongshou Liu ◽  
Xiangyu Chen
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Convex Set ◽  
The State ◽  
Analysis Method ◽  
Global Sensitivity ◽  
State Dependent ◽  
Sensitivity Analysis Method ◽  
Dependent Parameter ◽  
Input Variables

Convex set model is most widely applied around nonprobabilistic uncertainty description. This paper combines the convex model with global sensitivity analysis theory of variance, and then proposes an index based on convex set model and variance-based global sensitivity analysis method to illustrate the effect of the nonprobability variables on the dangerous degree. The proposed index consists of two parts, including the main and total indices. The main index can quantitatively reflect the effect of uncertainties of input variables on the variance of output response, and the total index reflects the influence of interaction with other variables in addition to the individual influence of input variables. Furthermore, an efficient state-dependent parameter solution for solving the variance-based global sensitivity analysis of nonprobabilistic convex uncertainty is given in this paper. The state-dependent parameter solution not only greatly improves the efficiency but also guarantees the computational accuracy, and the times of performance functions evaluation decrease from [Formula: see text] in single-loop Monte Carlo solution to 2048 in the state-dependent parameter method. Finally, three numerical examples and a finite element example are used to verify the feasibility and rationality of the proposed method.

A modified variance-based importance measure and its solution by state dependent parameter

2012 ◽  
Vol 227 (1) ◽  
pp. 3-15
Author(s):  
Wenbin Ruan ◽  
Zhenzhou Lu ◽  
Longfei Tian
Keyword(s):  
The State ◽  
Importance Measure ◽  
Parameter Method ◽  
Analysis Method ◽  
Output Response ◽  
State Dependent ◽  
Importance Analysis ◽  
Dependent Parameter ◽  
Input Variables ◽  
State Dependent Parameter Method

To overcome the disadvantage of traditional variance-based importance measures, i.e. the effects of different realizations of input variables on output response may mutually counteract each other, a modified variance-based importance measure is presented for importance analysis of the input variables. The proposed measure analyses the importance of the input variables comprehensively in terms of the expectation and variance of the output response. Compared with the traditional variance-based importance analysis method, the modified importance measure indices not only reflect the old one, but also provide a very useful supplement for it. Furthermore, combined with the advantages of the state dependent parameter model, a solution to the proposed measure indices is provided. Several examples are introduced to show that the modified importance measure is more comprehensive and reasonable, and the solution based on the state dependent parameter method can improve computational efficiency considerably with acceptable precision.

Modeling of a magneto-rheological damper: An improved multi-state-dependent parameter estimation approach

2019 ◽  
Vol 30 (8) ◽  
pp. 1178-1188 ◽  
Author(s):  
Abbas-Ali Zamani ◽  
Saeed Tavakoli ◽  
Sadegh Etedali ◽  
Jafar Sadeghi
Keyword(s):  
Parameter Estimation ◽  
Structural Control ◽  
Reference Model ◽  
Estimation Method ◽  
Operating Conditions ◽  
Training Data ◽  
State Dependent ◽  
Wide Range ◽  
Dependent Parameter ◽  
Magneto Rheological

A magneto-rheological damper, which is controlled by a magnetic field, is an effective smart damping device in structural control. Because of the complexity of its dynamics, however, the modeling and control of magneto-rheological dampers is still a challenging problem. In this article, an improved multi-state-dependent parameter estimation method is introduced and employed for magneto-rheological damper modeling. To provide a comprehensive training data, a modified Bouc–Wen model is used as the reference model. According to various tests, the proposed multi-state-dependent parameter model can predict the magneto-rheological damper response over a wide range of operating conditions fairly accurately.

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