Probabilistic identification of underground soil stratification using cone penetration tests
Why this work is in the frame
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Bibliographic record
Abstract
This paper develops Bayesian approaches for underground soil stratum identification and soil classification using cone penetration tests (CPTs). The uncertainty in the CPT-based soil classification using the Robertson chart is modeled explicitly in the Bayesian approaches, and the probability that the soil belongs to one of the nine soil types in the Robertson chart based on a set of CPT data is formulated using the maximum entropy principle. The proposed Bayesian approaches contain two major components: a Bayesian model class selection approach to identify the most probable number of underground soil layers and a Bayesian system identification approach to simultaneously estimate the most probable layer thicknesses and classify the soil types. Equations are derived for the Bayesian approaches, and the proposed approaches are illustrated using a real-life CPT performed at the National Geotechnical Experimentation Site (NGES) at Texas A&M University, USA. It has been shown that the proposed approaches properly identify the underground soil stratification and classify the soil type of each layer. In addition, as the number of model classes increases, the Bayesian model class selection approach identifies the soil layers progressively, starting from the statistically most significant boundary and gradually zooming into less significant ones with improved resolution. Furthermore, it is found that the evolution of the identified soil strata as the model class increases provides additional valuable information for assisting in the interpretation of CPT data in a rational and transparent manner.
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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