Quasi-site-specific multivariate probability distribution model for sparse, incomplete, and three-dimensional spatially varying soil data

ABSTRACT

In a previous work, the first two authors proposed a data-driven method that can construct a site-specific multivariate probability density function model for soil properties using sparse, incomplete, and spatially variable site investigation data. The spatial variability was limited to the depth direction (horizontal variability was not considered). This data-driven method is referred to as GPR-MUSIC-X. In the current paper, two improvements with respect to GPR-MUSIC-X are made. First, the one-dimensional spatial variability considered by GPR-MUSIC-X is extended to three-dimensional spatial variability (denoted by GPR-MUSIC-3X). Second, a hierarchical Bayesian model (HBM) is adopted to learn the cross-correlation (correlation among different soil parameters) behaviour of generic sites in a soil database accounting for site differences (or uniqueness), and the learning outcome is incorporated into GPR-MUSIC-3X. The resulting model is a quasi-site-specific model (denoted by HBM-MUSIC-3X) because it not only is based on site-specific data but also is informed by the soil database in a manner sensitive to site uniqueness. A case history is used to illustrate the effectiveness of the proposed HBM-MUSIC-3X.

KEYWORDS:

• Data-centric geotechnics
• data-driven site characterisation
• MUSIC-3X
• hierarchical Bayesian model
• quasi-site-specific model

Acknowledgements

The first author would like to thank the gracious support from the Ministry of Science and Technology of Taiwan (106-2221-E-002-084-MY3, 107-2221-E-002-053-MY3 & 109-2221-E-002-029-MY3). The last author was supported on related studies by the US National Science Foundation (grant number CMMI-1931069) during the course of this research; any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the US National Science Foundation. 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 304 dB (http://140.112.12.21/issmge/Database_2010.htm) used in this study and making it available for scientific inquiry.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Ministry of Science and Technology, Taiwan [grant number 106-2221-E-002-084-MY3,107-2221-E-002-053-MY3,109-2221-E-002-029-MY3]; US National Science Foundation [grant number CMMI-1931069].