Dictionary Learning of Spatial Variability at a Specific Site Using Data from Other Sites
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 150, Issue 9
Abstract
Due to time, budget, and/or technical constraints, geotechnical site investigation data from a specific site are often limited and sparse, leading to a long-lasting challenge in characterization of spatially varying geotechnical properties. During preliminary stages of site characterization, geotechnical data from neighboring sites or sites with similar geological conditions are often collected and used as valuable prior knowledge in geotechnical engineering practice. Nevertheless, existing methods for spatial variability characterization often rely solely on site-specific data and cannot effectively incorporate prior knowledge or existing databases. To address this issue, this study proposes a novel machine learning method that systematically combines sparsely measured data at a specific site with existing data from neighboring sites or sites with similar geological settings for characterization of property spatial variability in a data-driven manner. The proposed method starts with the construction of a dictionary that draws the dominant spatially varying patterns from a property measured at sites with similar geology under a dictionary learning framework. Leveraging the developed dictionary, the spatial variability of a property is interpreted from sparse site-specific measurements using Bayesian learning. The effectiveness of the proposed method is demonstrated using real data, and improved performance over existing methods is observed.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The work described in this paper was supported by grants from the Research Grant Council of Hong Kong Special Administrative Region (Project No.: CityU 11203322), the Science and Technology Development Fund, Macao SAR (Project No.: 001/2024/SKL), the National Natural Science Foundation of China (Grant No.: 42307215), and the Shenzhen Science and Technology Innovation Commission (Shenzhen-Hong Kong-Macau Science and Technology Project (Category C) No.: SGDX20210823104002020), China. The financial support is gratefully acknowledged.
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Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 150 • Issue 9 • September 2024
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© 2024 American Society of Civil Engineers.
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Received: Oct 23, 2023
Accepted: Mar 11, 2024
Published online: Jun 25, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 25, 2024
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