Article2002

Latin Hypercube Sampling for Stochastic Finite Element Analysis

231 citations0/0 files storedJournal of Engineering Mechanics

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Probabilistic and Robust Engineering Design Geotechnical Engineering and Analysis Advanced Multi-Objective Optimization Algorithms

Abstract

A Latin hypercube sampling method, including a reduction of spurious correlation in input data, is suggested for stochastic finite element analysis. This sampling procedure strongly improves the representation of stochastic design parameters compared to a standard Monte Carlo sampling. As the correlation control requires the number of realizations to be larger than the number of stochastic variables in the problem, a principal component analysis is employed to reduce the number of stochastic variables. In many cases, this considerably relaxes the restriction on the number of realizations. The method presented offers the same general applicability as the standard Monte Carlo sampling method but is superior in computational efficiency.

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References

2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1979A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1982A distribution-free approach to inducing rank correlation among input variables 1986Random field finite elements 1988Neumann Expansion for Stochastic Finite Element Analysis 1997Uncertainties in probabilistic numerical analysis of structures and solids-Stochastic finite elements

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