Fast CPT-based soil stratification using reversible jump Markov chain Monte Carlo simulation
Bibliographic record
Abstract
Cone penetration test (CPT) is widely used in geotechnical site investigation. Several methods have been developed to automatically identify soil stratigraphy based on a single CPT sounding, which is an essential step in interpreting CPT data. Among existing methods, Bayesian methods allow probabilistic reasoning of soil stratigraphy from a given CPT sounding with explicit uncertainty quantification. However, Bayesian soil stratification methods based on CPT data tend to be computationally demanding or even prohibitive, particularly at sites with excessive soil strata due to the high dimensionality of soil stratification models. To address this issue, this study proposes a Bayesian method for fast soil stratification based on a single CPT sounding using a trans-dimensional sampling technique known as reversible jump Markov chain Monte Carlo (RJMCMC) simulation. The RJMCMC simulation is specifically tailored for Bayesian soil stratification by developing proposal distributions for three different Markov chain moves. The proposed method is illustrated and validated using benchmark examples and two real-life CPT soundings. It accomplishes CPT-based probabilistic soil stratification within 1–2 min. The computational time is generally unaffected by CPT sounding depth or the number of soil layers, making it feasible to stratify soil profiles with an excessive number of soil layers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".