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Global Buckling Reliability Analysis of Slender Network Arch Bridges: An Application of Monte Carlo-Based Estimation by Optimized Fitting

Rønnquist, Anders; Næss, Arvid
Chapter
Accepted version
Åpne
Global+Buckling+Reliability+Analysis+of+Slender+Network+Arch+Bridges+-+An+Application+of+Monte+Carlo-Based+Estimation+by+Optimized+Fitting.pdf (Låst)
Permanent lenke
http://hdl.handle.net/11250/2492972
Utgivelsesdato
2017
Metadata
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  • Institutt for konstruksjonsteknikk [1576]
  • Institutt for matematiske fag [1396]
  • Publikasjoner fra CRIStin - NTNU [19849]
Originalversjon
Springer Series in Reliability Engineering. 2017, 47-66.   https://doi.org/10.1007/978-3-319-52425-2
Sammendrag
Network arch bridges are extremely slender bridge structures with a very efficient 16 load-carrying structure. This configuration can carry loads that are several times greater 17 than traditional tied-arch bridges with vertical hangers. These bridges are seen as an 18 attractive structure due to their slenderness, which potentially also make them vulnerable to 19 global system buckling. Thus, the buckling reliability of network arch bridges is here further 20 investigated with emphasis on geometric and load uncertainties. In principle, the reliability of 21 structural systems can be accurately predicted by standard Monte Carlo simulation. This 22 method has several attractive features for structural system reliability. One is that the system 23 failure criterion is easy to control, almost irrespective of the complexity of the system. 24 However, the computational cost involved may be prohibitive for highly reliable structural 25 systems if standard Monte Carlo simulation is used. In this chapter a recently developed 26 enhanced Monte Carlo method has been applied for calculating the reliability. This method 27 drastically reduced the computational burden of the standard Monte Carlo approach and 28 thereby made it practically feasible to estimate the reliability of the bridge against buckling.
Utgiver
Springer
Tidsskrift
Springer Series in Reliability Engineering

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