Soil Stratification and Spatial Variability Estimated Using Sparse Modeling and Gaussian Random Field Theory
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 3
Abstract
We propose a method for simultaneously estimating the trend and random components of soil properties using the least absolute shrinkage and selection operator (LASSO) for sparse modeling without assuming any basis functions and Gaussian random field theory, respectively. The uncorrelated observation noise is also estimated at the same time. A two-step algorithm is introduced to avoid the shrinkage problem of LASSO. Numerical examples with random realizations show that the method avoids shrinkage. The proposed method requires four parameters, namely, the variances of the random component and observation error, autocovariance distance, and regularization parameter, for one-dimensional problems. We propose a method that uses Akaike’s information criterion or Bayesian information criterion and particle swarm optimization to determine these four parameters. It is shown that the detection ratio of the layer boundary is determined by the number of observation data and the difference between trend values. The proposed method is applied to actual cone penetration test data to estimate the trend component of the soil property.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, which are listed below:
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Some simulation codes
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All the synthetic data used in the numerical tests
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All the results presented in this manuscript
Acknowledgments
We 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. We would like to express our gratitude to Professor K-K. Phoon of National University of Singapore and Professor Jianye Ching of National Taiwan University for useful discussion and their comments regarding this research.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 3 • September 2021
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© 2021 American Society of Civil Engineers.
History
Received: Jun 2, 2020
Accepted: Mar 4, 2021
Published online: May 20, 2021
Published in print: Sep 1, 2021
Discussion open until: Oct 20, 2021
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