An efficient approximation algorithm for variance global sensitivity by Bayesian updating
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OpenAlex topics: Probabilistic and Robust Engineering Design · Structural Health Monitoring Techniques · Fatigue and fracture mechanics
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References (28)
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1. Borgonovo, E.: A new uncertainty importance measure. Reliab. Eng. Syst. Saf. 92(6), 771–784 (2006)10.1016/j.ress.2006.04.015 ↗
2. Borgonovo, E., Tarantola, S., Plischke, E., Morris, M.: Transformations and invariance in the sensitivity analysis of computer experiments. J. Royal Stat. Soci. Series B (Stat. Methodol.) 76(5), 925–947 (2014)10.1111/rssb.12052 ↗
3. Chiron, M., Morio, J., Dubreuil, S.: Local Sensitivity of failure probability through polynomial regression and importance sampling. Mathematics 11(20), 4357 (2023)10.3390/math11204357 ↗
4. Cukier, R., Fortuin, C., Shuler, K., Petschek, A., Schaibly, J.: Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients I Theory. J. Chem. Phys. 59(8), 3873–3878 (1973)10.1063/1.1680571 ↗
5. Feng, K., Lu, Z., Pang, C., Yun, W.: Efficient numerical algorithm of profust reliability analysis: an application to wing box structure. Aerosp. Sci. Technol. 80, 203–211 (2018)10.1016/j.ast.2018.07.009 ↗
6. Feng, K., Lu, Z., Xiao, S.: A new global sensitivity measure based on the elementary effects method. Comput. Struct. 229, 106183 (2020)10.1016/j.compstruc.2019.106183 ↗
7. Guo, Q., Liu, Y., Chen, B., Yao, Q.: A variable and mode sensitivity analysis method for structural system using a novel active learning Kriging model. Reliab. Eng. Syst. Saf. 206, 107285 (2021)10.1016/j.ress.2020.107285 ↗
8. Hamid, K., Ghasemi, H.: Quantifying the uncertainties in modeling soft composites via a multiscale approach. Int. J. Solids Struct. 256, 111959 (2022)10.1016/j.ijsolstr.2022.111959 ↗
9. Hao, W., Lu, Z., Li, L.: A new interpretation and validation of variance based importance measures for models with correlated inputs. Comput. Phys. Commun. 184(5), 1401–1413 (2013)10.1016/j.cpc.2013.01.007 ↗
10. Li, L., Lu, Z., Feng, J., Wang, B.: Moment-independent importance measure of basic variable and its state dependent parameter solution. Struct. Saf. 38, 40–47 (2012)10.1016/j.strusafe.2012.04.001 ↗
11. Liu, Q., Homma, T.: A new computational method of a moment-independent uncertainty importance measure. Reliab. Eng. Syst. Saf. 94(7), 1205–1211 (2009)10.1016/j.ress.2008.10.005 ↗
12. Liu, Y., Li, L.: Global reliability sensitivity analysis based on state dependent parameter method and efficient sampling techniques. Aerosp. Sci. Technol. 99, 105740 (2020)10.1016/j.ast.2020.105740 ↗
13. Liu, X., Liang, X.: Global sensitivity analysis of electromechanical coupling behaviors for flexoelectric nanostructure. Int. J. Mech. Mater. Des. 18(1), 1–17 (2021)
14. Liu, Y., Wang, L., Liu, D.: Uncertain dynamic topology optimization based on the interval reliability evaluation and equivalent static loads (ESLs) algorithm. Eng. Comput. 38, 5657–5674 (2022)10.1007/s00366-022-01737-z ↗
15. Norwak, A., Collins, K.: Reliability of structures. McGraw-Hill, New York (2000)
16. Ratto, M., Andrea, P., Peter, Y.: State Dependent Parameter meta-modelling and sensitivity analysis. Comput. Phys. Commun. 177(11), 863–876 (2007)10.1016/j.cpc.2007.07.011 ↗
17. Saltelli, A.: Sensitivity analysis for importance assessment, Risk analysis: an official publication of the Society for. Risk Anal. 22(3), 579–590 (2002)10.1111/0272-4332.00040 ↗
18. Seo, S., Kwak, M.: Efficient statistical tolerance analysis for general distributions using three-point information. Int. J. Prod. Res. 40(4), 931–944 (2002)10.1080/00207540110095709 ↗
19. Sobol, I.: Sensitivity analysis for non-linear mathematical models. Math. Model. Comput. Exper. 1, 407–414 (1993)
20. Sobol, I.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55(1), 271–280 (2001)10.1016/S0378-4754(00)00270-6 ↗
21. Sudret, B.: Global sensitivity analysis using polynomial chaos expansions. Reliab. Eng. Syst. Saf. 93(7), 964–979 (2008)10.1016/j.ress.2007.04.002 ↗
22. Tryoen, J., Congedo, P., Abgrall, R., Villedieu, N., Magin, T.: Bayesian-based method with metamodels for rebuilding freestream conditions in atmospheric entry flows. AIAA J. 52(10), 2190–2197 (2014)10.2514/1.J052831 ↗
23. Vu-Bac, N., Areias, P., Rabczuk, T.: A multiscale multisurface constitutive model for the thermo-plastic behavior of polyethylene. Polymer 105, 327–338 (2016)10.1016/j.polymer.2016.10.039 ↗
24. Vu-Bac, N., Zhuang, X., Rabczuk, T.: Uncertainty quantification for mechanical properties of polyethylene based on fully atomistic model. Materials 12(21), 3613–3613 (2019)10.3390/ma12213613 ↗
25. Yang, S., Yi, T., Qu, C.: Adaptive sampling-based bayesian model updating for bridges considering substructure approach. ASCE-ASME J. Risk Uncertain. Eng. Syst., Part a: Civil Eng. 9(3), 04023024 (2023)10.1061/AJRUA6.RUENG-1077 ↗
26. Yun, W., Lu, Z., He, P.: Parameter global reliability sensitivity analysis with meta-models: A probability estimation-driven approach. Aerosp. Sci. Technol. 106, 106040 (2020)10.1016/j.ast.2020.106040 ↗
27. Zhang, D., Shen, S., Jiang, C.: An advanced mixed-degree cubature formula for reliability analysis. Comput. Methods Appl. Mech. Eng. 400, 115521 (2022)10.1016/j.cma.2022.115521 ↗
28. Zhou, C., Zhao, H., Chang, Q., Ji, M., Li, C.: Reliability and global sensitivity analysis for an airplane slat mechanism considering wear degradation. Chin. J. Aeronaut. 34(1), 163–170 (2021)10.1016/j.cja.2020.09.048 ↗