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Probabilistic analysis of strip footings based on enhanced Kriging metamodeling
Author(s) -
El Haj AbdulKader,
Soubra AbdulHamid,
AlBittar Tamara
Publication year - 2019
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.2995
Subject(s) - metamodeling , kriging , monte carlo method , discretization , surrogate model , probabilistic logic , sensitivity (control systems) , algorithm , gaussian , random field , mathematics , uncertainty quantification , computer science , mathematical optimization , machine learning , statistics , engineering , mathematical analysis , physics , quantum mechanics , electronic engineering , programming language
Summary Two advanced Kriging metamodeling techniques were used to compute the failure probability of geotechnical structures involving spatially varying soil properties. These methods are based on a Kriging metamodel combined with a global sensitivity analysis that is called in literature Global Sensitivity Analysis‐enhanced Surrogate (GSAS) modeling for reliability analysis. The GSAS methodology may be used in combination with either the Monte Carlo simulation (MCS) or importance sampling (IS) method. The resulting Kriging metamodeling techniques are called GSAS‐MCS or GSAS‐IS. The objective of these techniques is to reduce the number of calls of the mechanical model as compared with the classical Kriging‐based metamodeling techniques (called AK‐MCS and AK‐IS) combining Kriging with MCS or IS. The soil uncertain parameters were assumed as non‐Gaussian random fields. EOLE methodology was used to discretize these random fields. The mechanical models were based on numerical simulations. Some probabilistic numerical results are presented and discussed.