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Research Papers: Structures and Safety Reliability

System Reliability Analysis by Monte Carlo Based Method and Finite Element Structural Models

[+] Author and Article Information
Bruno Gaspar

Centre for Marine Technology
and Engineering (CENTEC),
Instituto Superior Técnico,
Technical University of Lisbon,
Lisboa 1049-001, Portugal
e-mail: bruno.gaspar@mar.ist.utl.pt

Arvid Naess

Centre for Ships and Ocean Structures and
Department of Mathematical Sciences,
Norwegian University of Science and Technology,
Trondheim NO-7491, Norway
e-mail: arvidn@math.ntnu.no

Bernt J. Leira

Department of Marine Technology,
Norwegian University of Science and Technology,
Trondheim NO-7491, Norway
e-mail: Bernt.Leira@marin.ntnu.no

C. Guedes Soares

Centre for Marine Technology
and Engineering (CENTEC),
Instituto Superior Técnico,
Technical University of Lisbon,
Lisboa 1049-001, Portugal
e-mail: guedess@mar.ist.utl.pt

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received May 1, 2011; final manuscript received April 6, 2012; published online April 15, 2014. Assoc. Editor: Lance Manuel.

J. Offshore Mech. Arct. Eng 136(3), 031603 (Apr 15, 2014) (9 pages) Paper No: OMAE-11-1037; doi: 10.1115/1.4025871 History: Received May 01, 2011; Revised April 06, 2012

In principle, the reliability of complex structural systems can be accurately predicted by Monte Carlo simulation. This method has several attractive features for structural system reliability, the most important being that the system failure criterion is usually relatively easy to check almost irrespective of the complexity of the system. However, the computational cost involved in the simulation may be prohibitive for highly reliable structural systems. In this paper a new Monte Carlo based method recently proposed for system reliability estimation that aims at reducing the computational cost is applied. It has been shown that the method provides good estimates for the system failure probability with reduced computational cost. In a numerical example the usefulness and efficiency of the method to estimate the reliability of a system represented by a nonlinear finite element structural model is presented. To reduce the computational cost involved in the nonlinear finite element analysis the method is combined with a response surface model.

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References

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Figures

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Fig. 1

Geometry of the finite element model

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Fig. 2

Finite element mesh and welding-induced initial distortions (mean amplitudes with scale factor x50)

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Fig. 3

Average stress-strain curve for the mean value of the strength basic random variables for the corroded condition. Ultimate compressive strength point defined by εxu = 1.45 · 10−3 and σxu/σyd = 0.814, with σxu = 283.2 MPa.

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Fig. 4

Deformed shape and von Mises stress distribution at collapse for the mean value of the strength basic random variables for the corroded condition (displacement with scale factor x25)

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Fig. 5

Ultimate compressive strength obtained through nonlinear finite element analysis versus the corresponding values predicted by the response surface model: (a) corroded scantlings condition; (b) intact scantlings condition. The black dots represent ultimate compressive strength values at design points X*(λ), with 0 ≤ λ ≤ 1.

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Fig. 6

Error of the response surface model approximation at design points X*(λ), with 0 ≤ λ ≤ 1

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Fig. 7

Log plot of component failure probability for corroded scantlings. Parameters of the fitted optimal curve: q = 0.253, a = 11.509, b = 0.101 and c = 1.326.

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Fig. 8

Log plot of component failure probability for intact scantlings. Parameters of the fitted optimal curve: q = 0.091, a = 16.930, b = 0.198 and c = 1.183.

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Fig. 9

Log plot of system failure probability for corroded scantlings. Parameters of the fitted optimal curve: q = 0.309, a = 12.114, b = 0.228 and c = 1.304.

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Fig. 10

Log plot of system failure probability for intact scantlings. Parameters of the fitted optimal curve: q = 0.149, a = 17.165, b = 0.266 and c = 1.184.

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