Skip to main content
Log in

An adaptive failure boundary approximation method for reliability analysis and its applications

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

In practical engineering problems, accurate reliability assessment often is computationally expensive with time-consuming numerical models or simulation models. How to obtain an accurate reliability index with a fewer number of calls to original performance function in reliability analysis has become an important challenge. For the purpose of reducing the computational cost in reliability analysis, this work develops an adaptive failure boundary approximation method (AFBAM) by combining Kriging and uniform sampling with a new adaptive learning strategy. The proposed AFBAM makes full use of the binary classification feature of reliability analysis in the way that the failure boundary of the original model can be efficiently approximated. The number of experimental design samples is constantly updated by selecting informative samples with the proposed learning strategy. In order to ensure classification accuracy of the constructed Kriging model, a new stopping criterion is designed based on average misclassification probability and misclassification ratio. The proposed AFBAM technically makes reliability evaluation phase independent of adaptive iterative process, which greatly improves the efficiency of model refinement phase. At last, five examples involving nonlinearity problem, small failure probability problem and practical engineering problem are tested to verify the efficiency of the proposed AFBAM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Lemaire M (2010) Structural reliability. Wiley, London

    Google Scholar 

  2. Zeng P, Li TB, Jimenez R, Feng XD, Chen Yu (2018) Extension of quasi-Newton approximation-based SORM for series system reliability analysis of geotechnical problems. Eng Comput 34:215–224

    Article  Google Scholar 

  3. Choi SK, Grandhi RV, Canfield RA (2007) Reliability-based structural design. Springer, London

    MATH  Google Scholar 

  4. Ibrahim Y (1991) Observations on applications of importance sampling in structural reliability analysis. Struct Saf 9(4):269–281

    Article  Google Scholar 

  5. Au SK, Beck JL (2002) Important sampling in high dimensions. Struct Saf 25(2):139–163

    Article  Google Scholar 

  6. Angelis MD, Patelli E, Beer M (2015) Advanced line sampling for efficient robust reliability analysis. Struct Saf 52:170–182

    Article  Google Scholar 

  7. Pradlwarter HJ, Schuëller GI, Koutsourelakis PS, Charmpis DC (2007) Application of line sampling simulation method to reliability benchmark problems. Struct Saf 29(3):208–221

    Article  Google Scholar 

  8. Au SK, Beck JL (2001) Estimation of small failure probabilities in high dimensions by subset simulation. Probab Eng Mech 16(4):263–277

    Article  Google Scholar 

  9. Au SK (2005) Reliability-based design sensitivity by efficient simulation. Comput Struct 83(14):1048–1061

    Article  Google Scholar 

  10. Yang IT, Hsieh Y-H (2013) Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization. Eng Comput 29:151–163

    Article  Google Scholar 

  11. Basudhar A, Missoumy S (2010) Reliability assessment using probabilistic support vector machines (PSVMs). In: 51st AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, 12–15

  12. Pan Rongjiang, Skala Vaclav (2011) A two-level approach to implicit surface modeling with compactly supported radial basis functions. Eng Comput 27:299–307

    Article  Google Scholar 

  13. Li X, Gao WK, Gu LX, Gong CL, Jing Z, Su H (2017) A cooperative radial basis function method for variable-fidelity surrogate modeling. Struct Multidisc Optim 56:1077–1092

    Article  Google Scholar 

  14. Papadopoulos V, Giovanis DG, Lagaros ND, Papadrakakis M (2012) Accelerated subset simulation with neural networks for reliability analysis. Comput Methods Appl Mech Eng 223:70–80

    Article  MathSciNet  Google Scholar 

  15. Amiri M, Amnieh HB, Hasanipanah M, Khanli LM (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32:631–644

    Article  Google Scholar 

  16. Simpson TW, Peplinski JD, Koch PN, Allen JK (2001) Metamodels for computer-based engineering design: survey and recommendations. Eng Comput 17:129–150

    Article  Google Scholar 

  17. Kleijnen JPC (2009) Kriging metamodeling in simulation: a review. Eur J Oper Res 192(3):707–716

    Article  MathSciNet  Google Scholar 

  18. Queipo Néstor V, Arévalo Carlos J, Pintos Salvador (2005) The integration of design of experiments, surrogate modeling and optimization for thermoscience research. Eng Comput 20:309–315

    Article  Google Scholar 

  19. Yondo R, Andres E, Valero E (2018) A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses. Prog Aerosp Sci 96:23–61

    Article  Google Scholar 

  20. Xiong F, Xiong Y, Chen W, Yang S (2009) Optimizing Latin hypercube design for sequential sampling of computer experiments. Eng Optimn 41(8):793–810

    Article  Google Scholar 

  21. Qian Peter Z G (2012) Sliced latin hypercube designs. J Am Stat Assoc 107(497):393–399

    Article  MathSciNet  Google Scholar 

  22. Viana FAC, Gerhard V, Vladimir B (2010) An algorithm for fast optimal Latin hypercube design of experiments. Int J Numer Methods Eng 82:135–156

    Article  MathSciNet  Google Scholar 

  23. Sheikholeslami R, Razavi S (2017) Progressive latin hypercube sampling: an efficient approach for robust sampling-based analysis of environmental models. Environ Model Softw 93:109–126

    Article  Google Scholar 

  24. Bichon BJ, Eldred MS, Swiler LP, Mahadevan S, McFarland JM (2008) Efficient global reliability analysis for nonlinear implicit performance functions. AIAA J 46:2459–2468

    Article  Google Scholar 

  25. Echard B, Gayton N, Lemaire M (2011) AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation. Struct Saf 33(2):145–154

    Article  Google Scholar 

  26. Echard B, Gayton N, Lemaire M (2013) A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models. Reliab Eng Syst Saf 111:232–240

    Article  Google Scholar 

  27. Huang X, Chen J, Zhu H (2016) Assessing small failure probabilities by AK–SS: an active learning method combining Kriging and Subset simulation. Struct Saf 59:86–95

    Article  Google Scholar 

  28. Zheng P, Wang CM, Zong Z, Wang L (2017) A new active learning method based on the learning function U of the AK-MCS reliability analysis method. Eng Struct 148:185–194

    Article  Google Scholar 

  29. Lv ZY, Lu ZZ, Wang P (2015) A new learning function for Kriging and its applications to solve reliability problems in engineering. Comput Math Appl 70:1182–1197

    Article  MathSciNet  Google Scholar 

  30. Basudhar A, Missoum S (2008) Adaptive explicit decision functions for probabilistic design and optimization using support vector machines. Comput Struct 86:1904–1917

    Article  Google Scholar 

  31. Pan Q, Dias D (2017) An efficient reliability method combining adaptive support vector machine and Monte Carlo simulation. Struct Saf 67:85–95

    Article  Google Scholar 

  32. Song KL, Zhang YG, Yu XS, Song BF (2019) A new sequential surrogate method for reliability analysis and its applications in engineering. IEEE Access 7:60555–60571

    Article  Google Scholar 

  33. Gaspar B, Teixeira AP, Guedes SC (2017) Adaptive surrogate model with active refinement combining Kriging and a trust region method. Reliab Eng SystSaf 165:277–291

    Article  Google Scholar 

  34. Sun Z, Wang J, Li R, Tong C (2017) LIF: a new Kriging based learning function and its application to structural reliability analysis. Reliab Eng Syst Saf 157:152–165

    Article  Google Scholar 

  35. Lophaven SN, Nielsen HB, Sondergaard J (2002) DACE, a matlab Kriging toolbox, version 2.0. Tech. Rep. IMM-TR-2002-12; Technical University of Denmark; 2002a

  36. Lebrun Régis, Dutfoy Anne (2009) Do Rosenblatt and Nataf isoprobabilistic transformation really differ? Probab Eng Mech 24(4):577–584

    Article  Google Scholar 

  37. Kocis L, Whiten WJ (1997) Computational investigations of low-discrepancy sequences. ACM Trans Math Softw 23(2):266–294

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China [Grant No. 51675428] and the Fundamental Research Funds for the Central Universities [Grant No. 3102015 BJ (II) JL01].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yugang Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, K., Zhang, Y., Zhuang, X. et al. An adaptive failure boundary approximation method for reliability analysis and its applications. Engineering with Computers 37, 2457–2472 (2021). https://doi.org/10.1007/s00366-020-01011-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01011-0

Keywords

Navigation