Probabilistic analysis of strip footings based on enhanced Kriging metamodeling

Abstract

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.

Metamodelling for geotechnical reliability analysis with noisy and incomplete models

“…The utility function is here formulated per sample. A global utility (e.g Hu and Mahadevan 2016;El Haj, Soubra, and Al-Bittar 2019). would require a global sensitivity analysis, which is computationally impractical due to excessive computational costs.The utility function is based on the most likely difference in the probability of mis-classification F( − |ĝ|/sĝ) before and after adding a learning candidate sample u i to the training data:…”