3D Probabilistic Site Characterization by Sparse Bayesian Learning
Publication: Journal of Engineering Mechanics
Volume 146, Issue 12
Abstract
In this paper, the sparse Bayesian learning (SBL) approach previously proposed for the characterization of one-dimensional (1D) soil spatial variability is extended to a more realistic three-dimensional (3D) setting. Direct extension is not computationally feasible because of significant runtime associated with inverting very large matrices and errors associated with computing their determinants. Based on the separability assumption in the autocorrelation function, the current paper successfully extends the SBL to 3D that is computable in practice. The numerical errors associated with large matrices are also mitigated. The second contribution of the current paper is a new efficient method of simulating conditional random fields in 3D based on a dense-lattice assumption. The analysis results for two real case histories show that it is now computationally feasible to characterize the statistical uncertainties in the autocorrelation parameters and trend function as well as to simulate conditional random field samples for 3D problems using the proposed method. To our knowledge, this is the first time we achieve realism in probabilistic site characterization and practicality in runtime at the same time.
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Data Availability Statement
All codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to thank the members of the TC304 Committee on Engineering Practice of Risk Assessment & Management of the International Society of Soil Mechanics and Geotechnical Engineering for developing the database 304dB (http://140.112.12.21/issmge/Database_2010.htm) used in this study and making it available for scientific inquiry. The first author would like to thank the Ministry of Science and Technology of Taiwan for the research grant 106-2221-E-002-084-MY3. The third author extends his appreciation to the Institute for Risk and Reliability, Leibniz University, and the funding from the Alexander von Humboldt Foundation for providing the support to complete this paper.
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Published In
Journal of Engineering Mechanics
Volume 146 • Issue 12 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Oct 4, 2019
Accepted: Jun 12, 2020
Published online: Sep 29, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 28, 2021
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