Kriging-Based Reliability Analysis of Strip Footings Resting on Spatially Varying Soils
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 144, Issue 10
Abstract
The probabilistic analysis of geotechnical structures presenting spatial variability in the soil properties is generally performed using Monte Carlo simulation (MCS) methodology. Despite being robust and accurate, MCS has low efficiency when considering the small failure probabilities encountered in practice. This is because it is very time-expensive in such cases due to the large number of simulations required to calculate a small failure probability with a small value of the coefficient of variation of this failure probability. In order to reduce the number of calls of the mechanical model when performing a probabilistic analysis, this paper uses the active learning reliability method combining kriging and Monte Carlo simulation (AK-MCS). This method is shown to be very efficient because the obtained probability of failure is very accurate, needing only a small number of calls to the computationally expensive mechanical model compared with MCS methodology. This study involves a probabilistic analysis at the ultimate limit state of a strip footing resting on a spatially varying soil using the AK-MCS approach. The soil cohesion and angle of internal friction are considered as random fields. The mechanical model is based on numerical simulations using the finite-difference code . The obtained probabilistic numerical results are presented and discussed.
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Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 144 • Issue 10 • October 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: May 27, 2017
Accepted: May 1, 2018
Published online: Jul 24, 2018
Published in print: Oct 1, 2018
Discussion open until: Dec 24, 2018
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