Conversion, prediction, and application of strength and stiffness parameters for grouted reconstructed rock mass
Bibliographic record
Abstract
Ultrasonic detection has emerged as a rapid method for acquiring rock mass sound velocity and converting it into an elastic modulus parameter, a pivotal technique for investigating the in-situ mechanical properties of rock masses. Despite its significance, accurately deducing rock mass strength from elastic modulus remains a formidable challenge and a pressing issue in the realm of protorock parameter research. This study introduces an innovative artificial intelligence-driven methodology for transforming elastic modulus and strength parameters specific to coal measures through rigorous data analysis and experimental validation. By integrating two illustrative engineering cases, we explore the complexities of water inrush and floor heave issues encountered in tunnels traversing fault zones. The novel strength parameter calculation approach is benchmarked against previous studies, highlighting its superior advantages in terms of effectiveness and applicability. In essence, this research offers a comprehensive framework and practical workflow for translating in-situ acoustic parameter-derived elastic modulus into rock mass strength, serving as a valuable resource for future endeavors in mine water control research.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".