Skip to main content
Log in

A generalized moving least square–based response surface method for efficient reliability analysis of structure

  • Research Paper
  • Published:
Structural and Multidisciplinary Optimization Aims and scope Submit manuscript

Abstract

To improve the efficiency and accuracy of traditional least squares method–based polynomial response surface method (RSM) for reliability analysis of structure, the application of various adaptive metamodeling approaches is notable. The moving least squares method (MLSM)–based RSM is the simplest one and found to be effective in this regard. But, its performance in reliability analysis of structure largely depends on the proper choice of the parameter of weight function involved. In the present study, a generalized scheme to appropriately obtain the hyper-parameter of the MLSM-based RSM to approximate implicit responses of structure for reliability analysis is proposed. The algorithm is hinged on the fact that for reliability analysis, one is interested in the sign of the approximated limit state function (LSF) rather than its magnitude. Thereby, it is sufficient to obtain the hyper-parameter for which the first derivative of the probability of failure as obtained from the approximated LSF with respect to the hyper-parameter is zero. The effectiveness of the proposed algorithm is elucidated through three numerical examples. The improvement achieved by the proposed MLSM-based RSM has been compared with the reliability results obtained by the MLSM-based RSM considering the commonly recommended value of the hyper-parameter and also by the approach where the parameters are obtained by leave one out cross-validation procedure.

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

Similar content being viewed by others

References

  • Breitkopf P, Naceur H, Rassineux A, Villon P (2005) Moving least squares response surface approximation: formulation and metal forming applications. Comput Struct 83:1411–1428

    Article  Google Scholar 

  • Bucher C, Most T (2008) A comparison of approximate response functions in structural reliability analysis. Probab Eng Mech 23:154–163

    Article  Google Scholar 

  • Chakraborty S, Chowdhury R (2016) Assessment of polynomial correlated function expansion for high-fidelity structural reliability analysis. Struct Saf 59:9–19

    Article  Google Scholar 

  • Chojaczyk AA, Teixeira AP, Neves LC, Cardoso JB, Soares CG (2015) Review and application of artificial neural networks models in reliability analysis of steel structures. Struct Saf 52:78–89

    Article  Google Scholar 

  • Ditlevsen O, Madsen HO (1996) Structural reliability methods. John Wiley and Sons Ltd., Chichester

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Elhewy AH, Mesbahi E, Pu Y (2006) Reliability analysis of structures using neural network method. Probab Eng Mech 21:44–53

    Article  Google Scholar 

  • Fang KT, Lin DK, Winke P, Zhang Y (2000) Uniform design: theory and application. Technometrics 42(3):237–248

    Article  MathSciNet  Google Scholar 

  • Gavin HP, Yau SC (2007) High-order limit state functions in the response surface method for structural reliability analysis. Struct Saf 30:162–179

    Article  Google Scholar 

  • Ghosh S, Chakraborty S (2017) Simulation based efficient seismic fragility analysis of existing structures. Earthq Struct 12(5):569–581

    Google Scholar 

  • Ghosh S, Roy A, Chakraborty S (2018) Support vector regression based metamodeling for seismic reliability analysis of structures. Appl Math Model 64:584–602

    Article  MathSciNet  Google Scholar 

  • Goel T, Haftka RT, Shyy W, Queipo NV (2007) Ensemble of surrogates. Struct Multidiscip Optim 33(3):199–216

    Article  Google Scholar 

  • Goswami S, Ghosh S, Chakraborty S (2016) Reliability analysis of structures by iterative improved response surface method. Struct Saf 60:56–66

    Article  Google Scholar 

  • Guo Z, Bai G (2009) Application of least squares support vector machine for regression to reliability analysis. Chin J Aeronaut 22(2):160–166

    Article  Google Scholar 

  • Haldar A, Mahadevan S (2000) Reliability assessment using stochastic finite element analysis. John Wiley and Sons, NY

    Google Scholar 

  • Haussler-Combe U (2001)Elementfreie Galerkin-Verfahren: Grundlagen und Einsatzmoglichkeiten zur Berechnung von Stahlbetontragwerken. Habilitation-Thesis, University of Karlsruhe, Germany

  • Haussler-Combe U, Korn C (1998) An adaptive approach with the element-free-Galerkin method. Comput Methods Appl Mech Eng 162:203–222

    Article  MathSciNet  Google Scholar 

  • Hurtado JE, Alvarez DA (2003) A classification approach for reliability analysis with stochastic finite element modelling. J Struct Eng ASCE 129:1141–1149

    Article  Google Scholar 

  • Jiang Y, Luo J, Liao G, Zhao Y, Zhang J (2015) An efficient method for generation of uniform support vector and its application in structural failure function fitting. Struct Saf 54:1–9

    Article  Google Scholar 

  • Kabasi S, Chakraborty S (2019) An efficient moving least squares based response surface method for reliability analysis of structures, 29th European safety and reliability conference, Hannover, Germany

  • Kang SC, Koh HM, Choo JF (2010) An efficient response surface method using moving least squares approximation for structural reliability analysis. Probab Eng Mech 25:365–371

    Article  Google Scholar 

  • Karutz H (2000) Adaptive Kopplung der Elementfreien Galerkin-Methodemit der Methode der FinitenElementebeiRissfortschrittsproblemen. Ph.D. Thesis Ruhr-Universitat Bochum, Germany 2000

  • Kaymaz I (2005) Application of Kriging method to structural reliability problems. Struct Saf 27:133–151

    Article  Google Scholar 

  • Keshtegar B (2017) A hybrid conjugate finite-step length method for robust and efficient reliability analysis. Appl Math Model 45:226–237

    Article  MathSciNet  Google Scholar 

  • Kim SH, Na SW (1997) Response surface method using vector projected sampling points. Struct Saf 19:3–19

    Article  Google Scholar 

  • Kim C, Wang S, Choi KK (2005) Efficient response surface modeling by using moving least-squares method and sensitivity. AIAA J 43(1):2404–2411

    Article  Google Scholar 

  • Kwon OS, Elnashai AS (2006) The effect of material and ground motion uncertainty on the seismic vulnerability curves of RC structure. Eng Struct 28(2):289–303

    Article  Google Scholar 

  • Mann NR, Schafer RE, Singpurwalla ND (1974) Methods for statistical analysis of reliability and life data. John Wiley & Sons Inc, New York

    MATH  Google Scholar 

  • Melchers RE (1999) Structural reliability analysis and prediction. John Wiley and Sons, Chichester

    Google Scholar 

  • Mohammed RK, Felician C (2020) Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide. Struct Multidiscip Optim 61:159–186

    Article  Google Scholar 

  • Most T, Bucher C (2005) A moving least squares weighting function for the element-free Galerkin method which almost fulfils essential boundary condition. Struct Eng Mech 21(3):315–332

    Article  Google Scholar 

  • Notin A, Gayton N, Dulong JL, Lemaire M, Villon P, Jaffal H (2010) RPCM: a strategy to perform reliability analysis using polynomial chaos and resampling. Eur J Comp Mech 19(8):795–830

    Article  Google Scholar 

  • Roy A, Manna R, Chakraborty S (2019) Support vector regression based metamodeling for structural reliability analysis. Probab Eng Mech 55:78–89

    Article  Google Scholar 

  • Schӧbi R, Sudret B, Marelli S (2017) Rare event estimation using polynomial chaos-Kriging. ASCE-ASME J Risk Uncertain Eng Syst Part A: Civ Eng 3(2):D4016002

    Google Scholar 

  • Su H, Chen Z, Wen Z (2016) Performance improvement method of support vector machine-based model monitoring dam safety. Struct Control Health Monit 23:252–266

    Article  Google Scholar 

  • Taflanidis AA, Cheung SH (2012) Stochastic sampling using moving least squares response surface approximations. Probab Eng Mech 2:216–224

    Article  Google Scholar 

  • Toropov VV, Scharamm U, Sahai A, Jones RD, Zeguer T (2005) Design optimization and stochastic analysis based on the moving least squares method. 6th World Cong. of Struct. Multidisc. Opti., Rio de Janeiro, Brazil

  • Youn BD, Choi KK (2004) A new response surface methodology for reliability-based design optimization. Comput Struct 82:241–245

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Subrata Chakraborty.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Replication of results

All the necessary information for generating the data sets for the problems are presented in the manuscript. The first author of this manuscript is pursuing his doctoral study and the codes can be available from the first author on request.

Additional information

Responsible Editor: Byeng D Youn

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

Kabasi, S., Roy, A. & Chakraborty, S. A generalized moving least square–based response surface method for efficient reliability analysis of structure. Struct Multidisc Optim 63, 1085–1097 (2021). https://doi.org/10.1007/s00158-020-02743-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00158-020-02743-9

Keywords

Navigation