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Hybrid Response Surface Function-Based Metamodeling of Response Approximation for Reliability Analysis

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Reliability, Safety and Hazard Assessment for Risk-Based Technologies

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Response surface method (RSM) is mostly adopted to overcome the computational challenge of Monte Carlo simulation (MCS)-based reliability assessment of system comprising implicit limit state function (LSF). In the present study, a hybrid response surface function (HRSF) based on exponential approximation and second-order polynomial estimation is investigated for improved response approximation for reliability analysis. The method calibrates each random variable according to the response function using an exponential function having a varying parameter that attempts to accommodate the nonlinearity of each variable in the LSF. The exponential function is further regressed using conventional quadratic polynomial model. The effectiveness of the proposed HRSF-based approach is elucidated numerically by considering several saturated designs and uniform design schemes. The performance of the proposed procedure is demonstrated by comparing the proposed response approximation and the usual polynomial RSM-based approximation with that of obtained by the most accurate direct MCS technique.

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References

  1. N.R. Mann, R.E. Schafer, N.D. Singpurwalla, Methods for Statistical Analysis of Reliability and Life Data (Wiley, New York, 1974)

    MATH  Google Scholar 

  2. B. Richard, C. Cremona, L. Adelaide, A response surface method based on support vector machines trained with an adaptive experimental design. Struct. Saf. 39, 14–21 (2012)

    Article  Google Scholar 

  3. L. Faravelli, Response surface approach for reliability analyses. J. Eng. Mech. 115(2), 2763–2781 (1989)

    Article  Google Scholar 

  4. C.G. Bucher, U. Bourgund, A fast and efficient response surface approach for structural reliability problems. Struct. Saf. 7(1), 57–66 (1990)

    Article  Google Scholar 

  5. M.R. Rajashekhar, B.R. Ellingwood, A new look at the response surface approach for reliability analysis. Struct. Saf. 12(3), 205–220 (1993)

    Article  Google Scholar 

  6. S.H. Kim, S.W. Na, Response surface method using vector projected sampling points. Struct. Safety 19, 3–19 (1997)

    Article  Google Scholar 

  7. I. Kaymaz, C.A. McMahon, A response surface method based on weighted regression for structural reliability analysis. Probab. Eng. Mech. 20, 11–17 (2005)

    Article  Google Scholar 

  8. D.L. Allaix, V.I. Carbone, An improvement of the response surface method. Struct. Saf. 33, 165–172 (2011)

    Article  Google Scholar 

  9. W. Zhao, Z. Qiu, An efficient response surface method and its application to structural reliability and reliability-based optimization. Finite Elem. Anal. Des. 67, 34–42 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. G. Su, L. Peng, L. Hu, A Gaussian process-based dynamic surrogate model for complex engineering structural reliability analysis. Struct. Saf. 68, 97–109 (2017)

    Article  Google Scholar 

  12. B. Keshtegar, O. Kisi, Modified response-surface method: new approach for modeling pan evaporation. J. Hydrol. Eng. 22(10) (2017). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001541

    Article  Google Scholar 

  13. B. Keshtegar, S. Heddam, Modelling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput. Appl. 1–12 (2017)

    Google Scholar 

  14. N.R. Draper, H. Smith, Applied Regression Analysis (Wiley-Interscience, 1998). ISBN 0-471-17082-8

    Google Scholar 

  15. C.J. Willmott, K. Matsuura, Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30, 79–82 (2005)

    Article  Google Scholar 

  16. A.D. Kiureghian, M. De Stefano, Efficient algorithm for second order reliability analysis. J. Eng. Mech. 117 (1991)

    Article  Google Scholar 

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Correspondence to Sounak Kabasi .

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Kabasi, S., Chakraborty, S. (2020). Hybrid Response Surface Function-Based Metamodeling of Response Approximation for Reliability Analysis. In: Varde, P., Prakash, R., Vinod, G. (eds) Reliability, Safety and Hazard Assessment for Risk-Based Technologies. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-9008-1_47

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  • DOI: https://doi.org/10.1007/978-981-13-9008-1_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9007-4

  • Online ISBN: 978-981-13-9008-1

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