Efficient Simulation of 2D Non-Stationary CPT Profiles from an Incomplete Data Set Using Bayesian Compressive Sensing Methods
Publication: Geo-Risk 2023
ABSTRACT
Cone penetration test (CPT) is one of the most widely used in situ methods for characterizing the spatial variability of site characterization, due to its rapidity, affordability, and repeatability. It is often encountered in practice that some CPTs probe deeper than others, and that some CPT soundings may contain missing data due to presence of gravel-sized particles or intentional bypassing of gravelly soil layers. Furthermore, the number of CPT soundings in a cross-section is often sparse, although the number of data points along the depth is adequate. In these cases, it is difficult and challenging to estimate spatially varying soil property at locations without CPT soundings, especially for non-stationary CPT within multi-layers. While certain methods have been proposed hoping to address these concerns, they are frequently constrained by either stationary assumption of data, autocorrelation function forms, or computational issues. This study presents a Bayesian compressive sensing-based machine learning methods for this issue, with both numerical and real-world CPT data for validation and demonstration. Results show that the proposed method performs reasonably well in simulating 2D non-stationary CPT profiles from incomplete data set.
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REFERENCES
Cai, G., Liu, S., Tong, L., and Du, G. (2009). “Assessment of direct CPT and CPTU methods for predicting the ultimate bearing capacity of single piles.” Eng. Geol., 104(3-4), 211–222.
Ching, J., Yang, Z., and Phoon, K.-K. (2021). “Dealing with Nonlattice Data in Three-Dimensional Probabilistic Site Characterization.” J. Eng. Mech., 147(5), 06021003.
Christian, J. T., Ladd, C. C., and Baecher, G. B. (1994). “Reliability applied to slope stability analysis.” J. Geotech. Engrg., 120(12), 2180–2207.
Gong, W., Luo, Z., Juang, C. H., Huang, H., Zhang, J., and Wang, L. (2014). “Optimization of site exploration program for improved prediction of tunneling-induced ground settlement in clays.” Comput. Geotech., 56, 69–79.
Guan, Z., Wang, Y., and Zhao, T. (2021). “Delineating the spatial distribution of soil liquefaction potential in a cross-section from limited cone penetration tests.” Soil Dyn. Earthq. Eng., 145, 106710.
Lenz, J. A., and Baise, L. G. (2007). “Spatial variability of liquefaction potential in regional mapping using CPT and SPT data.” Soil Dyn. Earthq. Eng., 27(7), 690–702.
MathWorks. (2023). “MATLAB: the language of technical computing.”, (http://www.mathworks.com/products/matlab/) [accessed at 01/02/2023].
Mayne, P. W., Christopher, B. R., and DeJong, J. (2002). “Subsurface investigations—geotechnical site characterization.” National Highway Institute, Federal Highway Administration, Washington, D.C.
Phoon, K.-K., Kulhawy, F. H., and Grigoriu, M. D. (2003). “Development of a reliability-based design framework for transmission line structure foundations.” J. Geotech. Geoenviron. Eng., 129(9), 798–806.
Yang, Z., and Ching, J. (2021). “Simulation of three-dimensional random field conditioning on incomplete site data.” Eng. Geol., 281, 105987.
Zhao, T., and Wang, Y. (2020). “Non-parametric simulation of non-stationary non-gaussian 3D random field samples directly from sparse measurements using signal decomposition and Markov Chain Monte Carlo (MCMC) simulation.” Reliab. Eng. Syst. Saf., 203, 107087.
Zhao, T., Hu, Y., and Wang, Y. (2018). “Statistical interpretation of spatially varying 2D geo-data from sparse measurements using Bayesian compressive sampling.” Eng. Geol., 246, 162–175.
Zhao, T., Xu, L., and Wang, Y. (2020). “Fast non-parametric simulation of 2D multi-layer cone penetration test (CPT) data without pre-stratification using Markov Chain Monte Carlo simulation.” Eng. Geol., 273, 105670.
Zhao, T., Wang, Y., Lu, S., and Xu, L. (2023). “Fast stratification of geological cross-section from CPT results with missing data using multitask and modified Bayesian compressive sensing.” Can. Geotech. J., In press.
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Published online: Jul 20, 2023
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Compression
- Continuum mechanics
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Field tests
- Geomechanics
- Geotechnical engineering
- Geotechnical investigation
- Methodology (by type)
- Models (by type)
- Numerical analysis
- Numerical methods
- Penetration tests
- Soil mechanics
- Soil properties
- Solid mechanics
- Statistical analysis (by type)
- Structural dynamics
- Tests (by type)
- Two-dimensional models
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