A new method on ANN for variance based importance measure analysis of correlated input variables

Structural Safety

Volumn 38, Issue , 2012, Pages 56-63

  Hao, Wenrui ^a   Lu, Zhenzhou ^a   Wei, Pengfei ^a   Feng, Jun ^b   Wang, Bintuan ^b  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b Aviation Industry Corporation of China (AVIC)   (China)

Author keywords

Artificial neural networks; Correlated contribution; Correlated input variables; Importance matrix; Importance measure; Sensitivity analysis; Uncorrelated contribution; Variance decomposition

Indexed keywords

CORRELATED CONTRIBUTION; IMPORTANCE MEASURE; INPUT VARIABLES; UNCORRELATED CONTRIBUTION; VARIANCE DECOMPOSITION;

NEURAL NETWORKS; SENSITIVITY ANALYSIS;

PRECISION ENGINEERING;

EID: 84861088412     PISSN: 01674730     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.strusafe.2012.02.003     Document Type: Article

References (18)

1. Guo J., Du X.P. Sensitivity analysis with mixture of epistemic and aleatory uncertainties. AIAA J 2007, 45(9):2337-2349.
2. Xu Chonggang, Gertner George Zdzislaw Uncertainty and sensitivity analysis for models with correlated parameters. Reliab Eng Syst Saf 2008, 93(6):1563-1573.
3. Aven T., Nokland T.E. On the use of uncertainty importance measures in reliability and risk analysis. Reliab Eng Syst Saf 2010, 95(2):127-133.
4. Doksum K., Samarov A. Nonparametric estimation of global functionals and a measure of the explanatory power of covariates in regression. Ann Statist 1995, 23(5):1443-1473.
5. Satelli A. The role of sensitivity analysis in the corroboration of models and its link to model structural and parametric uncertainty. Reliab Eng Syst Saf 1997, 57(1):1-4.
6. Sobol I.M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 2001, 55(1):271-280.
7. Iman R.L., Hora S.C. A robust measure of uncertainty importance for use in fault tree system analysis. Risk Anal 1990, 10(3):401-406.
8. Satelli A. Sensitivity analysis for importance assessment. Risk Anal 2002, 22(3):579-590.
9. Borgonovo E. A new uncertainty importance measure. Reliab Eng Syst Saf 2007, 92(6):771-784.
10. Cui L.J., Lu Z.Z., Zhao X.P. Moment-independent importance measure of basic random variable and its probability density evolution solution. Sci China Technol Sci 2010, 53(4):1138-1145.
11. Hosni E.A., Mesbahi E., Pu Y. Reliability analysis of structures using neural network method. Probab Eng Mech 2006, 21(1):44-53.
12. Gomesa H.M., Awruch A.M. Comparison of response surface and neural network with other methods for structural reliability analysis. Struct Saf 2004, 26(1):49-67.
13. Deng J., Gu D.S., Li X.B., Yue Z.Q. Structural reliability analysis for implicit performance functions using artificial neural network. Struct Saf 2005, 27(1):25-48.
14. Cardoso J.B., Almeida J.R., Dias J.M., Coelho P.G. Structural reliability analysis using Monte Carlo simulation and neural networks. Adv Eng Software 2008, 39(6):505-513.
15. Oscar M., Ricardo O.F., Laura M.Q., Marcelo R. Structural optimization for performance-based design in earthquake engineering: applications of neural networks. Struct Saf 2009, 31(6):490-499.
16. Song B.F. A study on method for computing structural system failure probability 1994, Northwestern Polytechnical University, Xi'an, [in Chinese].
17. Agarwal B.D., Broutman L.J. Analysis and performance of fiber composites 1980, John Wiley Sons, Inc.
18. Christensen R.M. Mechanics of composite materials 1979, John Wiley sons, Inc.