Volume 37, Issue 2 (3-2019)
2019, 37(2): 41-60 |
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Saravani S, Keshtegar B. Random - weighted Monte Carlo Simulation Method for Structural Reliability Analysis. Computational Methods in Engineering 2019; 37 (2) :41-60
URL: http://jcme.iut.ac.ir/article-1-706-en.html
URL: http://jcme.iut.ac.ir/article-1-706-en.html
1- Department of Civil Engineering, University of Zabol, Zabol, Iran.
2- Department of Civil Engineering, University of Zabol, Zabol, Iran. ,bkeshtegar@uoz.ac.ir
2- Department of Civil Engineering, University of Zabol, Zabol, Iran. ,
Abstract: (4140 Views)
The computational burdens and more accurate approximations for the estimation of the failure probability are the main concerns in the structural reliability analyses. The Monte Carlo simulation (MCS) method can simply provide an accurate estimation for the failure probability, but it is a time-consuming method for complex reliability engineering problems with a low failure probability and may efficiently approximate the failure probability. In this paper, the efficiency of MCS for the computations of the performance function is improved using a random-weighted method known as the random-weighted MCS (RWMC) method. By using the weighted exponential function, the weights of random data points generated by MCS are adjusted by selecting the random point in the design space. The convergence performances including the computational burdens for evaluating the limit sate function and the accuracy of failure probabilities of RWMC are compared with MCS by using several nonlinear and complex mathematical and structural problems with normal and no-normal random variables. The results indicate that the proposed RWMC method can estimate the accurate results with the less computational burdens, about 100 to 1000 times faster than MCS
Keywords: Monte Carlo Simulation, Structural reliability analysis, Failure possibility, Random- weighted Monte Carlo simulation
Type of Study: Research |
Subject:
Special
Received: 2018/01/17 | Accepted: 2018/07/15 | Published: 2019/03/15
Received: 2018/01/17 | Accepted: 2018/07/15 | Published: 2019/03/15
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