A Bayesian Vine Algorithm for Geotechnical Site Characterization Using High Dimensional, Multivariate, Limited, and Missing Data-Reference-Cited by-同舟云学术

A Bayesian Vine Algorithm for Geotechnical Site Characterization Using High Dimensional, Multivariate, Limited, and Missing Data

Published:2024-07 Issue:7 Volume:150 Page:
ISSN:0733-9399
Container-title:Journal of Engineering Mechanics
language:en
Short-container-title:J. Eng. Mech.

Author:

Sharma Atma¹,Zhang Jie²,Spagnoli Giovanni³

Affiliation:

1. Research Associate, Dept. of Geotechnical Engineering and Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji Univ., Shanghai 200092, China (corresponding author).

2. Professor, Dept. of Geotechnical Engineering and Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji Univ., Shanghai 200092, China.

3. Team Lead Resources and Energy, Sweco GmbH, Zeche Katharina 6, Essen 45307, Germany.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JENMDT.EMENG-7460

Reference58 articles.

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2. Allen, T., A. Nowak, and R. Bathurst. 2005. Calibration to determine load and resistance factors for geotechnical and structural design. Washington, DC: Transportation Research Board.

3. Liquefaction Resistance of Soils from Shear-Wave Velocity

4. Baecher, G. 1983. Simplified geotechnical data analysis, 257–277. Dordrecht, Netherlands: Springer.

5. Vines--a new graphical model for dependent random variables

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