Data-Driven Development of Three-Dimensional Subsurface Models from Sparse Measurements Using Bayesian Compressive Sampling : A Benchmarking Study

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

26 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number04023010
Journal / PublicationASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume9
Issue number2
Online published9 Feb 2023
Publication statusPublished - Jun 2023

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. © 2023 American Society of Civil Engineers.

Research Area(s)

  • Bayesian compressive sensing/sampling (BCS), Benchmarking study, Cone penetration test (CPT), Three dimensional (3D) subsurface modeling

Citation Format(s)

Data-Driven Development of Three-Dimensional Subsurface Models from Sparse Measurements Using Bayesian Compressive Sampling: A Benchmarking Study. / Lyu, Borui; Hu, Yue; Wang, Yu.
In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 9, No. 2, 04023010, 06.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review