A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis
Tóm tắt
Từ khóa
Tài liệu tham khảo
Huang, 2008, Probabilistic uncertainty analysis by mean value first order saddlepoint approximation, Reliab Eng Syst Saf, 93, 325, 10.1016/j.ress.2006.10.021
Zhu, 2013, Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys, Probab Eng Mech, 34, 114, 10.1016/j.probengmech.2013.08.004
Du, 2004, First order saddlepoint approximation for reliability analysis, AIAA J, 42, 1199, 10.2514/1.3877
Melchers, 1999
Low, 2014, FORM, SORM, and spatial modeling in geotechnical engineering, Struct Saf, 49, 56, 10.1016/j.strusafe.2013.08.008
Hurtado, 2013, A method for enhancing computational efficiency in Monte Carlo calculation of failure probabilities by exploiting FORM results, Comput Struct, 117, 95, 10.1016/j.compstruc.2012.11.022
Okasha, 2016, An improved weighted average simulation approach for solving reliability-based analysis and design optimization problems, Struct Saf, 60, 47, 10.1016/j.strusafe.2016.01.005
Xiao, 2012, Unified uncertainty analysis by the mean value first order saddlepoint approximation, Struct Multidis Optim, 46, 803, 10.1007/s00158-012-0794-4
Rahman, 2006, A univariate approximation at most probable point for higher-order reliability analysis, Int J Solids Struct, 43, 2820, 10.1016/j.ijsolstr.2005.05.053
Papadopoulos, 2012, Accelerated subset simulation with neural networks for reliability analysis, Comput Methods Appl Mech Eng, 223-224, 70, 10.1016/j.cma.2012.02.013
Meng, 2015, Reliability-based multidisciplinary design optimization using subset simulation analysis and its application in the hydraulic transmission mechanism design, ASME J Mech Des, 137, 10.1115/1.4029756
Zhang, 2015, First and second order approximate reliability analysis methods using evidence theory, Reliab Eng Syst Saf, 137, 40, 10.1016/j.ress.2014.12.011
Okasha, 2016, An improved weighted average simulation approach for solving reliability-based analysis and design optimization problems, Struct Saf, 60, 47, 10.1016/j.strusafe.2016.01.005
Dubourg, 2014, Meta-model-based importance sampling for reliability sensitivity analysis, Struct Saf, 49, 27, 10.1016/j.strusafe.2013.08.010
Grooteman, 2008, Adaptive radial-based importance sampling method for structural reliability, Struct Saf, 30, 533, 10.1016/j.strusafe.2007.10.002
Wang, 2011, A generalized complementary intersection method for system reliability analysis, ASME J Mech Des, 133, 071003, 10.1115/1.4004198
Goswami, 2016, Reliability analysis of structures by iterative improved response surface method, Struct Saf, 60, 56, 10.1016/j.strusafe.2016.02.002
Kaymaz, 2005, A response surface method based on weighted regression for structural reliability analysis, Probab Eng Mech, 20, 11, 10.1016/j.probengmech.2004.05.005
Kang, 2010, An efficient response surface method using moving least squares approximation for structural reliability analysis, Probab Eng Mech, 25, 365, 10.1016/j.probengmech.2010.04.002
Hadid, 2017, Efficient response surface method for high-dimensional structural reliability analysis, Struct Saf, 68, 15, 10.1016/j.strusafe.2017.03.006
Crombecq, 2011, Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling, Eur J Oper Res, 214, 683, 10.1016/j.ejor.2011.05.032
Crombecq, 2009, A novel sequential design strategy for global surrogate modeling
Eason, 2014, Adaptive sequential sampling for surrogate model generation with artificial neural networks, Comput Chem Eng, 68, 220, 10.1016/j.compchemeng.2014.05.021
Bichon, 2011, Efficient surrogate models for reliability analysis of systems with multiple failure modes, Reliab Eng Syst Saf, 96, 1386, 10.1016/j.ress.2011.05.008
Bichon, 2008, Efficient global reliability analysis for nonlinear implicit performance functions, AIAA J, 46, 2459, 10.2514/1.34321
Echard, 2011, AK-MCS: An active learning reliability method combining Kriging and Monte Carlo simulation, Struct Saf, 33, 145, 10.1016/j.strusafe.2011.01.002
Zhu, 2016, Reliability analysis with Monte Carlo simulation and dependent Kriging predictions, ASME J Mech Des, 10.1115/1.4034219
Hu, 2016, Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis, Struct Multidis Optim, 53, 501, 10.1007/s00158-015-1347-4
Wen, 2016, A sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability, Reliab Eng Syst Saf, 153, 170, 10.1016/j.ress.2016.05.002
Sun, 2017, LIF: a new Kriging based learning function and its application to structural reliability analysis, Reliab Eng Syst Saf, 157, 152, 10.1016/j.ress.2016.09.003
Gaspar, 2017, Adaptive surrogate model with active refinement combining Kriging and a trust region method, Reliab Eng Syst Saf, 165, 277, 10.1016/j.ress.2017.03.035
Chojaczyk, 2015, Review and application of artificial neural networks models in reliability analysis, Struct Saf, 53, 78, 10.1016/j.strusafe.2014.09.002
Gorissen, 2010, A surrogate modeling and adaptive sampling toolbox for computer based design, J Mach Learn Res, 11, 2051
Xiao, 2014, A novel reliability method for structural systems with truncated random variables, Struct Saf, 50, 57, 10.1016/j.strusafe.2014.03.006
Graupe D. Principles of artificial neural networks (second ed.). Hackensack: World Scientific Publishing, 2007.
Sundar, 2016, Surrogate-enhanced stochastic search algorithms to identify implicitly defined functions for reliability analysis, Struct Saf, 62, 1, 10.1016/j.strusafe.2016.05.001
Srinivas, 2000, Multi-objective function optimization using nondominated sorting genetic algorithms, IEEE Trans Evol Comput, 2, 221
Kleijnen, 2004, Application-driven sequential design for simulation experiments: Kriging meta-modelling, J Oper Res Soc, 55, 876, 10.1057/palgrave.jors.2601747
Aute, 2013, Cross validation based single response adaptive design of experiments for Kriging metamodeling of deterministic computer simulations, Struct Multidis Optim, 48, 581, 10.1007/s00158-013-0918-5
Aute, 2008, Cross-validation based single response adaptive design of experiments for deterministic computer simulations
Golzari, 2015, Development of an adaptive surrogate model for production optimization, J Petrol Sci Eng, 133, 677, 10.1016/j.petrol.2015.07.012