Probabilistic generic transformation model between two rock mass properties: specific fracture energy and P-wave velocity
Bibliographic record
Abstract
This study proposed a probabilistic generic transformation model between two rock mass properties, specific fracture energy, Ev, and P-wave velocity, VP. To build the transformation model, 12 pairwise data sets of Ev and VP were collected from six different construction sites involving construction of mountain tunnels in Japan. This database is labeled as “RockMass/2/350”. A probabilistic transformation model was built based on a bivariate standard normal distribution with these 350 data points. The model is generic, because it is based on a variety of sites. The performance of the constructed transformation model was evaluated through a cross-validation. It was found that 98.2% of the validation data fell within the computed 95% confidence interval of the model estimation, and this result provides a preliminary validation of the probabilistic transformation model. Unlike existing deterministic transformation models for estimating VP from Ev, the proposed model can explicitly evaluate the transformation uncertainty with a quantitative metric such as a percentile. For practical application, a 3D model of the spatial distribution for Young’s modulus, E, was visualized based on the proposed transformation model. Since the proposed model is probabilistic, it can provide the spatial distribution for percentiles of E values. The constructed 3D model presented in this paper can be directly used as an input data for finite element or finite difference analysis, and probabilistic evaluation of excavation simulation is feasible based on the proposed probabilistic model. The quantitative information on such uncertainty can be useful in decision-making for tunnel constructions, such as selection of a cautious characteristic value.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".