References
1. Bucher CG, Bourgund U (1990) A fast and efficient response surface approach for structural
reliability problems. Struct Saf 7:57–66
2. Rajashekhar MR, Ellingwood BR (1993) A new look at the response surface approach for
reliability analysis. Struct Saf 12:205–220
3. Kim S, Na S (1997) Response surface method using vector projected sampling points. Struct
Saf 19:3–19
4. Kaymaz I, McMahon CA (2005) A response surface method based on weighted regression
for structural reliability analysis. Probab Eng Mech 20:11–17
5. Lee SH, Kwak BM (2006) Response surface augmented moment method for efficient
reliability analysis. Struct Saf 28:261–272
6. Allaix DL, Carbone VI (2011) An improvement of the response surface method. Struct Saf
33:165–172
7. Acharjee S, Zabaras N (2007) A non-intrusive stochastic Galerkin approach for modeling
uncertainty propagation in deformation processes. Comput Struct 85:244–254
8. Paffrath M, Wever U (2007) Adapted polynomial chaos expansion for failure detection.
J Comput Phys 226:263–281
9. Wei DL, Cui ZS, Chen J (2008) Uncertainty quantification using polynomial chaos
expansion with points of monomial cubature rules. Comput Struct 86:2102–2108
10. Blatman G, Sudret B (2011) Adaptive sparse polynomial chaos expansion based on least
angle regression. J Comput Phys 230:2345–2367
11. Scheremans L, Van GD (2005) Use of Kriging as meta-model in simulation procedures for
structural reliability. In: 9th International conference on structural safety and reliability
12. Kaymaz I (2005) Application of Kriging method to structural reliability problems. Struct Saf
27:133–151
13. Hyeon JB, Chai Lee B (2008) Reliability-based design optimization using a moment method
and a Kriging metamodel. Eng Optim 40:421–438
14. Echard B, Gayton N, Lemaire M (2011) AK-MCS: an active learning reliability method
combining Kriging and Monte Carlo Simulation. Struct Saf 33:145–154
15. Hurtado JE, Alvarez DA (2001) Neural-network-based reliability analysis: a comparative
study. Comput Methods Appl Mech Eng 191:113–132
16. Deng J, Gu D, Li X, Zhong Q (2005) Structural reliability analysis for implicit performance
functions using artificial neural network. Struct Saf 27:25–48
17. Gomes HM, Awruch AM (2004) Comparison of response surface and neural network with
other methods for structural reliability analysis. Struct Saf 26:49–67
18. Papadrakakis M, Lagaros ND (2002) Reliability-based structural optimization using neural
networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 191:3491–3507
19. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least
squares support vector machines. World Scientific Publishing Co. Pre.Ltd, London
20. Zhu P, Zhang Y, Chen G (2011) Metamodeling development for reliability-based design
optimization of automotive body structure. Comput Ind 62:729–741
21. Rocco CM, Moreno JA (2002) Fast Monte Carlo reliability evaluation using support vector
machine. Reliab Eng Syst Saf 76:237–243
22. Hurtado JE (2007) Filtered importance sampling with support vector margin: a powerful
method for structural reliability analysis. Struct Saf 29:2–15
23. Chen KY (2007) Forecasting systems reliability based on support vector regression with
genetic algorithms. Reliab Eng Syst Saf 92:423–432
218
Y. Guo et al.
1. Bucher CG, Bourgund U (1990) A fast and efficient response surface approach for structural
reliability problems. Struct Saf 7:57–66
2. Rajashekhar MR, Ellingwood BR (1993) A new look at the response surface approach for
reliability analysis. Struct Saf 12:205–220
3. Kim S, Na S (1997) Response surface method using vector projected sampling points. Struct
Saf 19:3–19
4. Kaymaz I, McMahon CA (2005) A response surface method based on weighted regression
for structural reliability analysis. Probab Eng Mech 20:11–17
5. Lee SH, Kwak BM (2006) Response surface augmented moment method for efficient
reliability analysis. Struct Saf 28:261–272
6. Allaix DL, Carbone VI (2011) An improvement of the response surface method. Struct Saf
33:165–172
7. Acharjee S, Zabaras N (2007) A non-intrusive stochastic Galerkin approach for modeling
uncertainty propagation in deformation processes. Comput Struct 85:244–254
8. Paffrath M, Wever U (2007) Adapted polynomial chaos expansion for failure detection.
J Comput Phys 226:263–281
9. Wei DL, Cui ZS, Chen J (2008) Uncertainty quantification using polynomial chaos
expansion with points of monomial cubature rules. Comput Struct 86:2102–2108
10. Blatman G, Sudret B (2011) Adaptive sparse polynomial chaos expansion based on least
angle regression. J Comput Phys 230:2345–2367
11. Scheremans L, Van GD (2005) Use of Kriging as meta-model in simulation procedures for
structural reliability. In: 9th International conference on structural safety and reliability
12. Kaymaz I (2005) Application of Kriging method to structural reliability problems. Struct Saf
27:133–151
13. Hyeon JB, Chai Lee B (2008) Reliability-based design optimization using a moment method
and a Kriging metamodel. Eng Optim 40:421–438
14. Echard B, Gayton N, Lemaire M (2011) AK-MCS: an active learning reliability method
combining Kriging and Monte Carlo Simulation. Struct Saf 33:145–154
15. Hurtado JE, Alvarez DA (2001) Neural-network-based reliability analysis: a comparative
study. Comput Methods Appl Mech Eng 191:113–132
16. Deng J, Gu D, Li X, Zhong Q (2005) Structural reliability analysis for implicit performance
functions using artificial neural network. Struct Saf 27:25–48
17. Gomes HM, Awruch AM (2004) Comparison of response surface and neural network with
other methods for structural reliability analysis. Struct Saf 26:49–67
18. Papadrakakis M, Lagaros ND (2002) Reliability-based structural optimization using neural
networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 191:3491–3507
19. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least
squares support vector machines. World Scientific Publishing Co. Pre.Ltd, London
20. Zhu P, Zhang Y, Chen G (2011) Metamodeling development for reliability-based design
optimization of automotive body structure. Comput Ind 62:729–741
21. Rocco CM, Moreno JA (2002) Fast Monte Carlo reliability evaluation using support vector
machine. Reliab Eng Syst Saf 76:237–243
22. Hurtado JE (2007) Filtered importance sampling with support vector margin: a powerful
method for structural reliability analysis. Struct Saf 29:2–15
23. Chen KY (2007) Forecasting systems reliability based on support vector regression with
genetic algorithms. Reliab Eng Syst Saf 92:423–432
218
Y. Guo et al.