A Bayesian Vine Algorithm for Geotechnical Site Characterization Using High Dimensional, Multivariate, Limited, and Missing Data
Publication: Journal of Engineering Mechanics
Volume 150, Issue 7
Abstract
Geotechnical site characterization using multivariate, limited (sparse), and missing (incomplete) data is an important but challenging task, particularly in high dimensions. Toward this problem, this study proposes a Bayesian vine algorithm. In the proposed algorithm, the task of Bayesian update in higher dimensions is translated into a series of lower-dimensional (usually ) update tasks using conditional correlation vine. This feature of the proposed algorithm makes it scalable and computationally efficient in higher dimensions. Multiple examples using two-dimensional (2D), five-dimensional (5D), 10-dimensional (10D), 20-dimensional (20D), 50-dimensional (50D), and 100-dimensional (100D) data are shown to demonstrate the capability of the proposed algorithm. The results suggest that the proposed algorithm can be used successfully for geotechnical site characterization. Even an ultrahigh 50D joint distribution with parameters (1,325) can be estimated in around 20 min. The proposed algorithm can handle multivariate data sets with limited and missing values and can also handle non-Gaussian multivariate joint distributions. The proposed algorithm only considers cross-correlation in the site data and doesn’t take into account spatial correlation.
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Data Availability Statement
All models and computer codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was substantially supported by the Shanghai Municipal Human Resources and Social Security Bureau (2023554), National Key Research and Development Program of China (2021YFB2600500), the National Natural Science Foundation of China (42072302, 52025094), the Key Innovation Team Program of MOST of China (2016RA4059), and Fundamental Research Funds for the Central Universities. The authors are grateful to Professor Jianye Ching, Taiwan University, Chinese Taipei, for his valuable suggestions on the manuscript. The authors also thank the TC304 Committee on Engineering Practice of Risk Assessment and Management of the International Society of Soil Mechanics and Geotechnical Engineering members for developing the database 304dB (TC304) used in this study and making it available for scientific inquiry. Finally, the authors thank the anonymous reviewers for their valuable comments on a previous version of this manuscript.
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Published In
Journal of Engineering Mechanics
Volume 150 • Issue 7 • July 2024
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© 2024 American Society of Civil Engineers.
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Received: Jul 10, 2023
Accepted: Feb 18, 2024
Published online: May 14, 2024
Published in print: Jul 1, 2024
Discussion open until: Oct 14, 2024
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