Data-Driven Site Characterization for Benchmark Examples Using Sparse Bayesian Learning
Publication: Geo-Risk 2023
ABSTRACT
In this paper, a data-driven site characterization method called the sparse Bayesian learning (SBL) method previously proposed by the author is benchmarked by a set of virtual ground examples and a real ground example of cone penetration test (CPT) data. The SBL method assumes a zero-mean prior Gaussian random field model for the spatial trend modeled by sparse basis functions. The accuracy of the SBL method in predicting the cone tip resistance (qt) of CPT is quantified by the root-mean-square prediction error (RMSE), whereas the accuracy in identifying soil layers is quantified by the identification rate (IR). The performance of SBL is compared with that of the GLasso method. It is found that SBL does not always outperform GLasso, and GLasso does not always outperform SBL, either. Nonetheless, SBL requires less computational cost than GLasso.
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REFERENCES
Ching, J., and Chen, Y. C. (2007). “Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection and model averaging.” ASCE Journal of Engineering Mechanics, 133(7), 816–832.
Ching, J., and Phoon, K. K. (2017). “Characterizing uncertain site-specific trend function by sparse Bayesian learning.” ASCE Journal of Engineering Mechanics, 143(7), 04017028.
Ching, J., and Phoon, K .K. (2019). “Impact of auto-correlation function model on the probability of failure.” ASCE Journal of Engineering Mechanics, 145(1), 04018123.
Ching, J., Huang, W. H., and Phoon, K. K. (2020). “3D Probabilistic site characterization by sparse Bayesian learning.” ASCE Journal of Engineering Mechanics, 146(12), 04020134.
Ching, J., Yang, Z. Y., and Phoon, K. K. (2021). “Dealing with non-lattice data in three-dimensional probabilistic site characterization.” ASCE Journal of Engineering Mechanics, 147(5), 06021003.
Guttorp, P., and Gneiting, T. (2006). “Studies in the history of probability and statistics XLIX on the Matérn correlation family.” Biometrika, 93(4), 989–995.
Jaksa, M. B., Kaggwa, W. S., and Brooker, P. I. (1999). “Experimental evaluation of the scale of fluctuation of a stiff clay.” Proceedings of the 8th International Conference on Application of Statistics and Probability, A.A. Balkema, Rotterdam, 415–422.
Liu, W. F., Leung, Y. F., and Lo, M. K. (2017). “Integrated framework for characterization of spatial variability of geological profiles.” Canadian Geotechnical Journal, 54(1), 47–58.
Phoon, K. K., Ching, J., and Shuku, T. (2021). “Challenges in data-driven site characterization.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 16(1), 114–126.
Phoon, K. K., Shuku, T., Ching, J., and Yoshida, I. (2022). “Benchmark examples for data-driven site characterization.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, In press.
Robertson, P. K. (2016). “Cone penetration test (CPT)-based soil behaviour type (SBT) classification system - an update.” Canadian Geotechnical Journal, 53(12), 1910–1927.
Tipping, M. E. (2001). “Sparse Bayesian learning and the relevance vector machine.” Journal of Machine Learning Research, 1, 211–244.
Wang, H., Wang, X., Wellmann, J. F., and Liang, R. Y. (2018). “Bayesian stochastic soil modeling framework using Gaussian Markov random fields.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(2), 04018014.
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Published In
Geo-Risk 2023
Pages: 438 - 445
History
Published online: Jul 20, 2023
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Benchmark
- Business management
- Conical bodies
- Design (by type)
- Engineering fundamentals
- Field tests
- Gaussian process
- Geometry
- Geotechnical data
- Geotechnical engineering
- Geotechnical investigation
- Load and resistance factor design
- Load factors
- Management methods
- Mathematics
- Penetration tests
- Practice and Profession
- Probability
- Statistical analysis (by type)
- Stochastic processes
- Structural design
- Tests (by type)
Authors
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