Comparison of Data-Driven Site Characterization Methods through Benchmarking: Methodological and Application Aspects
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9, Issue 2
Abstract
Site characterization is one of the most crucial steps for decision making in geotechnical engineering and to the fullest extent possible should be conducted based on objective data. The current reliance on engineering judgment to interpret data directly cannot exploit the rapid growth of data, machine learning, and other digital technologies. Data-driven site characterization (DDSC) has received much attention in an emerging field called data-centric geotechnics, because a knowledge of the ground is fundamental to geotechnical engineering. As a result, many DDSC methods have been developed recently. Differences and similarities between DDSC methods, however, have not been well studied in terms of methodological and application aspects. This paper proposes a comparison between three emerging DDSC methods from these methodological and application perspectives: (1) geotechnical lasso (Glasso), (2) geotechnical lasso with basis-functions (Glasso-BFs), and (3) Gaussian process regression (GPR). From a methodological perspective, this paper presents a unified Bayesian framework to derive these DDSC methods, in order to shed light on the methodological similarities and differences. From the application perspective, the prediction accuracy for the validation dataset and runtime cost of these three DDSC methods were compared through benchmarking. The differences in performance can be better understood within the unified framework. This paper further proposes a new benchmark involving complex intermixing of soil types, to test the three methods under more realistic and challenging field conditions, although the training and validation datasets remain synthetic.
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Data Availability Statement
All data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by JSPS KAKENHI Grant No. JP18K05880.
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Published In
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.
History
Received: Aug 6, 2022
Accepted: Dec 4, 2022
Published online: Jan 30, 2023
Published in print: Jun 1, 2023
Discussion open until: Jun 30, 2023
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Cited by
- Kok-Kwang Phoon, Takayuki Shuku, Future of Machine Learning in Geotechnics (FOMLIG), 5–6 Dec 2023, Okayama, Japan, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 10.1080/17499518.2024.2316882, 18, 1, (288-303), (2024).
- Zia ur Rehman, Trends and Challenges of Technology-Enhanced Learning in Geotechnical Engineering Education, Sustainability, 10.3390/su15107972, 15, 10, (7972), (2023).
- K. K. Phoon, L. M. Zhang, Z. J. Cao, Special issue on “Machine learning and AI in geotechnics”, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 10.1080/17499518.2023.2185938, 17, 1, (1-6), (2023).
- Takayuki Shuku, Kok-Kwang Phoon, Data-driven subsurface modelling using a Markov random field model, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 10.1080/17499518.2023.2181973, 17, 1, (41-63), (2023).
- Kok-Kwang Phoon, Takayuki Shuku, Jianye Ching, Ikumasa Yoshida, Benchmarking Data-Driven Site Characterization, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10.1061/AJRUA6.RUENG-1058, 9, 2, (2023).
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