Data-Driven Development of Three-Dimensional Subsurface Models from Sparse Measurements Using Bayesian Compressive Sampling: A Benchmarking Study
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VIEW CORRECTIONPublication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9, Issue 2
Abstract
With the rapid development of computing and digital technologies recently, three-dimensional (3D) subsurface models for accurate site characterization have received increasing attention, for example, with various data-driven methods developed for 3D subsurface modeling. This leads to a need for validating the 3D modeling results obtained from each method and comparing the performance of different methods in a fair and consistent manner. To address this need, a benchmarking study, which is often used in machine learning (ML), is presented in this study to compare the performance of different 3D subsurface modeling methods in four aspects, including accuracy, uncertainty, robustness, and computational efficiency. A suite of performance metrics is proposed for the four aspects above. Multiple sets of real cone penetration test (CPT) data are compiled in the benchmarking study for quantifying performance of 3D modeling methods using sparse measurements as input, a typical scenario in geotechnical practice. The benchmarking study is illustrated using an in-house software package called Analytics of Sparse Spatial Data based on Bayesian compressive sampling/sensing (ASSD-BCS), which can directly generate high-resolution 3D random field samples (RFSs) from sparse measurements. The evaluation results show that ASSD-BCS provides accurate estimates with quantified uncertainty from sparse measurements. In addition, ASSD-BCS exhibits remarkably high computational efficiency and performs robustly under different benchmarking cases.
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Data Availability Statement
All data, models, and code generated or used during the study are available from the corresponding author upon reasonable request. The BCS-based software package ASSD-BCS version 1.1 is available at https://sites.google.com/site/yuwangcityu/software-download/bayesian-compressive-samplingsensing-bcs.
Acknowledgments
The work described in this paper was supported by a grant from the Innovation and Technology Commission of Hong Kong Special Administrative Region (Project No. MHP/099/21), a grant from the Research Grants Council of the Hong Kong Special Administrative Region (Project No. CityU 11213119), and a grant from 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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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9 • Issue 2 • June 2023
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© 2023 American Society of Civil Engineers.
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Received: Apr 27, 2022
Accepted: Nov 2, 2022
Published online: Feb 9, 2023
Published in print: Jun 1, 2023
Discussion open until: Jul 9, 2023
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