CPT-Based Probabilistic Soil Characterization and Classification
Why this work is in the frame
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Bibliographic record
Abstract
Due to lack of soil sampling during conventional cone penetration testing, it is necessary to characterize and classify soils based on tip and sleeve friction values as well as pore pressure induced during and after penetration. Currently available semiempirical methods exhibit a significant variability in the estimation of soil type. Within the confines of this paper it is attempted to present a new probabilistic cone penetration test (CPT)-based soil characterization and classification methodology, which addresses the uncertainties intrinsic to the problem. For this purpose, a database composed of normalized corrected cone tip resistance (qt,1,net) , normalized friction ratio (FR) , fines content (FC), liquid limit (LL), plasticity index (PI), and soil type based on the unified soil classification system was complied. Soil classification was performed by laboratory testing of the standard penetration test disturbed samples retrieved from the boreholes within mostly 2m of each CPT hole. The resulting database was probabilistically assessed through Bayesian updating methodology allowing full and consistent representation of relevant uncertainties, including (1) model imperfection; (2) statistical uncertainty; and (3) inherent variability. As a conclusion, different sets of FC, LL, PI, and A -line boundary curves along with a new CPT-based, simplified soil classification scheme are proposed in the qt,1,net and FR domain. Probabilistic uses of the proposed models are illustrated through a set of illustrative examples.
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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 | 0.000 |
| 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