High-dimensional model representation for structural reliability analysis

This paper presents a new computational tool for predicting failure probability of structural/mechanical systems subject to random loads, material properties, and geometry. The method involves high‐dimensional model representation (HDMR) that facilitates lower‐dimensional approximation of the origin...

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Vydáno v:Communications in numerical methods in engineering Ročník 25; číslo 4; s. 301 - 337
Hlavní autoři: Chowdhury, Rajib, Rao, B. N., Prasad, A. Meher
Médium: Journal Article
Jazyk:angličtina
Vydáno: Chichester, UK John Wiley & Sons, Ltd 01.04.2009
Wiley
Témata:
Computational techniques
Exact sciences and technology
failure probability
Fracture mechanics (crack, fatigue, damage...)
Fundamental areas of phenomenology (including applications)
high-dimensional model representation
Mathematical methods in physics
moving least squares
Physics
response surface
Solid mechanics
Structural and continuum mechanics
structural reliability
Monte Carlo method
Statistical analysis
Probabilistic approach
Rupture
moving least squares
Structural reliability
Modeling
high-dimensional model representation
System with n degrees of freedom
Least squares method
Mechanical system
Random load
Response surface
Implicit function theorem
failure probability
Non linear effect
Random medium
Quantitative analysis
Structural analysis
Input output model
ISSN:1069-8299, 1099-0887
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Shrnutí:This paper presents a new computational tool for predicting failure probability of structural/mechanical systems subject to random loads, material properties, and geometry. The method involves high‐dimensional model representation (HDMR) that facilitates lower‐dimensional approximation of the original high‐dimensional implicit limit state/performance function, response surface generation of HDMR component functions, and Monte Carlo simulation. HDMR is a general set of quantitative model assessment and analysis tools for capturing the high‐dimensional relationships between sets of input and output model variables. It is a very efficient formulation of the system response, if higher‐order variable correlations are weak, allowing the physical model to be captured by the first few lower‐order terms. Once the approximate form of the original implicit limit state/performance function is defined, the failure probability can be obtained by statistical simulation. Results of nine numerical examples involving mathematical functions and structural mechanics problems indicate that the proposed method provides accurate and computationally efficient estimates of the probability of failure. Copyright © 2008 John Wiley & Sons, Ltd.
Bibliografie:istex:380E41709102E2FEC6F4B5419CCEB79B57A8F507
ark:/67375/WNG-F2ZJWP50-4
ArticleID:CNM1118
Board of Research in Nuclear Sciences, India - No. 2004/36/39-BRNS/2332
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1069-8299
1099-0887
DOI:10.1002/cnm.1118