Voxel Interpolation of Geotechnical Properties and Soil Classification Based on Empirical Bayesian Kriging and Best-Fit Convergence Function
To support bearing capacity estimates, this study develops and tests a geoprocessing workflow for predicting soil properties using Empirical Bayesian Kriging 3D and a classification function. The model covers a 183 m × 185 m × 24 m site in Astana (Kazakhstan), based on 16 boreholes (15–24 m deep) an...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/14/2452 |
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| Summary: | To support bearing capacity estimates, this study develops and tests a geoprocessing workflow for predicting soil properties using Empirical Bayesian Kriging 3D and a classification function. The model covers a 183 m × 185 m × 24 m site in Astana (Kazakhstan), based on 16 boreholes (15–24 m deep) and 77 samples. Eight geotechnical properties were mapped in 3D voxel models (812,520 voxels at 1 m × 1 m × 1 m resolution): cohesion (c), friction angle (<i>φ</i>), deformation modulus (<i>E</i>), plasticity index (<i>PI</i>), liquidity index (<i>LI</i>), porosity (<i>e</i>), particle size (<i>PS</i>), and particle size distribution (<i>PSD</i>). Stratification patterns were revealed with ~35% variability. Maximum φ (34.9°), <i>E</i> (36.6 MPa), and <i>PS</i> (1.29 mm) occurred at 8–16 m; <i>c</i> (33.1 kPa) and <i>PSD</i> peaked below 16 m, while <i>PI</i> and <i>e</i> were elevated in the upper and lower strata. Strong correlations emerged in pairs <i>φ</i>-<i>E</i>-<i>PS</i> (0.91) and <i>PI</i>-<i>e</i> (0.95). Classification identified 10 soil types, including one absent in borehole data, indicating the workflow’s capacity to detect hidden lithologies. Predicted fractions of loams (51.99%), sandy loams (22.24%), and sands (25.77%) matched borehole data (52%, 26%, 22%). Adjacency analysis of 2,394,873 voxel pairs showed homogeneous zones in gravel–sandy soils (28%) and stiff loams (21.75%). The workflow accounts for lateral and vertical heterogeneity, reduces subjectivity, and is recommended for digital subsurface 3D mapping and construction design optimization. |
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| ISSN: | 2075-5309 |