Quantifying uncertainty of in situ horizontal stress and geotechnical parameters using a Bayesian inference approach for pressuremeter tests
Bibliographic record
Abstract
Understanding in situ horizontal stress is crucial for various applications in deep ground, including oil drilling, hydraulic fracturing, and nuclear waste repository design. The pressuremeter has gained increasing attention as a tool for characterizing in situ stress fields and engineering properties of soil and rock. However, quantifying the uncertainties associated with in situ horizontal stress and geotechnical parameters remains a challenging task. In this study, we propose a Bayesian inference approach formulated by an objective function that calculates the logarithm of the probability density function using observed and predicted data to address this problem. This approach integrates the analytical solution and the finite-difference numerical model into the Bayesian model. The Bayesian inference approach consists of two phases: (1) using the maximum a posteriori method for point estimation, and (2) employing Markov chain Monte Carlo sampling to obtain parameter statistics from posterior distributions. Compared to frequentist statistical methods, the Bayesian inference approach provides a natural way to incorporate prior knowledge, update our beliefs by conditioning on the observed data, and facilitate exploratory analysis of Bayesian models with various diagnostic tools. This provides a robust and adaptable framework for addressing uncertainty in the study of in situ stress fields and geotechnical parameters using the pressuremeter.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".