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Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures

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  • Mahdi Shadab Far
  • Yuan Wang

Abstract

Structural load types, on the one hand, and structural capacity to withstand these loads, on the other hand, are of a probabilistic nature as they cannot be calculated and presented in a fully deterministic way. As such, the past few decades have witnessed the development of numerous probabilistic approaches towards the analysis and design of structures. Among the conventional methods used to assess structural reliability, the Monte Carlo sampling method has proved to be very convenient and efficient. However, it does suffer from certain disadvantages, the biggest one being the requirement of a very large number of samples to handle small probabilities, leading to a high computational cost. In this paper, a simple algorithm was proposed to estimate low failure probabilities using a small number of samples in conjunction with the Monte Carlo method. This revised approach was then presented in a step-by-step flowchart, for the purpose of easy programming and implementation.

Suggested Citation

  • Mahdi Shadab Far & Yuan Wang, 2016. "Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, May.
  • Handle: RePEc:hin:jnlmpe:5726565
    DOI: 10.1155/2016/5726565
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    Cited by:

    1. Pepper, Nick & Crespo, Luis & Montomoli, Francesco, 2022. "Adaptive learning for reliability analysis using Support Vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Mahdi Shadabfar & Cagri Gokdemir & Mingliang Zhou & Hadi Kordestani & Edmond V. Muho, 2020. "Estimation of Damage Induced by Single-Hole Rock Blasting: A Review on Analytical, Numerical, and Experimental Solutions," Energies, MDPI, vol. 14(1), pages 1-24, December.

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