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

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 a...

Full description

Bibliographic Details
Main Authors: Mahdi Shadab Far, Yuan Wang
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2016/5726565
id doaj-c3c65ee4541c4b58ac789e9af0228532
record_format Article
spelling doaj-c3c65ee4541c4b58ac789e9af02285322020-11-24T22:04:07ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472016-01-01201610.1155/2016/57265655726565Approximation of the Monte Carlo Sampling Method for Reliability Analysis of StructuresMahdi Shadab Far0Yuan Wang1School of Civil and Transportation Engineering, Hohai University, Nanjing, Jiangsu 210098, ChinaSchool of Civil and Transportation Engineering, Hohai University, Nanjing, Jiangsu 210098, ChinaStructural 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.http://dx.doi.org/10.1155/2016/5726565
collection DOAJ
language English
format Article
sources DOAJ
author Mahdi Shadab Far
Yuan Wang
spellingShingle Mahdi Shadab Far
Yuan Wang
Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures
Mathematical Problems in Engineering
author_facet Mahdi Shadab Far
Yuan Wang
author_sort Mahdi Shadab Far
title Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures
title_short Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures
title_full Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures
title_fullStr Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures
title_full_unstemmed Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures
title_sort approximation of the monte carlo sampling method for reliability analysis of structures
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2016-01-01
description 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.
url http://dx.doi.org/10.1155/2016/5726565
work_keys_str_mv AT mahdishadabfar approximationofthemontecarlosamplingmethodforreliabilityanalysisofstructures
AT yuanwang approximationofthemontecarlosamplingmethodforreliabilityanalysisofstructures
_version_ 1725830446268284928