Reliability sensitivity analysis of sandwich panels using Monte Carlo simulation

Document Type : Original Article

Authors

1 Ph.D. Student in Structural Engineering, Faculty of civil engineering, Semnan University, Semnan, Iran

2 Associate Proffessor, Faculty of Civil Engineering, Semnan University, Semnan, Iran

3 Associate professor, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran

Abstract

In many practical applications of structural reliability analysis, one of the preferred sciences is the sensitivity analysis of failure probability based on design parameters under the limit state function. This information is needed, for example, in design optimization based on reliability. The design parameters are calculated by first- and second-order reliability methods FORM\SORM to sensitivity analysis of the probability of failure approximately. Therefore, in many cases, these methods are very inaccurate or difficult to solve problems. Accordingly, the Monte Carlo simulation method is very useful for determining the reliability sensitivity parameters. The derivation probability of failure into the parameters of the limit state function is calculated by integrating the surface of the performance function. By using the Monte Carlo simulation to determine this integral will not be efficient due to the small portion probability of failure and will have a high computational cost. Hence, some methods can be used to estimate Monte Carlo simulations that reduce the computational cost. One of these methods is linear sampling method. In this paper, an approximate method is used to determine the surface integral in terms of a domain integral. The integration domain can be calculated using standard Monte Carlo simulations or importance sampling simulations. This article presents two practical examples of sandwich panels to determine the effectiveness of parameters random variables are used in the design.

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References

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Journal of Structural and Construction Engineering
Volume 10, Issue 6 - Serial Number 71
September 2023
Pages 115-132

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History

  • Receive Date: 16 July 2022
  • Revise Date: 23 October 2022
  • Accept Date: 14 November 2022

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