Constructing Quasi-Site-Specific Multivariate Probability Distribution Using Hierarchical Bayesian Model
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VIEW CORRECTIONPublication: Journal of Engineering Mechanics
Volume 147, Issue 10
Abstract
In geotechnical engineering, it is challenging to construct a site-specific multivariate probability distribution model for soil/rock properties because the site-specific data are usually sparse and incomplete. In contrast, there are abundant generic soil/rock data in the literature for the construction of a generic multivariate probability distribution model, but this model is typically biased and/or imprecise for a specific site. A hybridization method has been proposed to combine these two sources of soil/rock data (site-specific data and a generic database) to produce a quasi-site-specific model, but this method is essentially heuristic. In the current paper, a more rational method that exploits the geologic origin of soil/rock data is proposed. There is a tendency for data to be more similar within a single site and less similar between sites. This is called site uniqueness in geotechnical engineering practice, but no data-driven methods exist to quantify this data feature currently. The hierarchical Bayesian model (HBM) is a natural model to exploit this group information. The grouping criterion can be site localization, soil/rock types, or others. This paper only studies the group criterion based on site localization. This means that a generic database is now viewed as a collection of data groups labeled by qualitative site labels. This site label does not contain any quantitative information such as GPS location, it merely demarcates each group as distinct. The novel contribution is the development of an efficient HBM with closed-form conditional probabilities based on suitably chosen conjugate priors that can handle multivariate, uncertain and unique, sparse, incomplete, and potentially corrupted (MUSIC) data containing site labels. Numerical comparisons between the hybridization method (which cannot incorporate group information) and HBM show that even the simple qualitative knowledge that data belong to a geographically constrained site can improve the estimation of soil/rock properties. The GPS location of each site is not needed.
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Data Availability Statement
All database data, case history data, and computer 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 their support (Project No. 107-2221-E-002-053-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 work on this paper.
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Published In
Journal of Engineering Mechanics
Volume 147 • Issue 10 • October 2021
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© 2021 American Society of Civil Engineers.
History
Received: Dec 3, 2019
Accepted: Mar 29, 2021
Published online: Jul 27, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 27, 2021
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