Cone penetration test (CPT)-based subsurface soil classification and zonation in two-dimensional vertical cross section using Bayesian compressive sampling
Why this work is in the frame
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Bibliographic record
Abstract
A novel method is developed in this study for soil classification and zonation in a two-dimensional (2D) vertical cross section using cone penetration tests (CPTs). A CPT is usually performed vertically and the number of CPT soundings in a site is often limited in geotechnical engineering practice. It is, therefore, difficult to properly interpret CPT results along the horizontal direction or accurately estimate the horizontal correlation length of CPT data. The method proposed in this study bypasses the difficulty in estimating horizontal correlation length and provides proper identification of subsurface soil stratification (i.e., soil layer number is constant along horizontal direction) and zonation (i.e., soil layer number varies along horizontal direction) in a 2D vertical cross section directly from a limited number of CPT soundings. The proposed method consists of three key elements: 2D interpolation of CPT data using 2D Bayesian compressive sampling; determination of soil behavior type (SBT) using a SBT chart at every location in the 2D section, including locations with measurements and unsampled locations; and soil layer or zone delineation using an edge detection method. Both simulated and real data examples are used to illustrate the proposed method. Results show that the method performs well even when only five sets of CPT soundings are available.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it