A New Approach for Rock Strength Estimation Through a Semi-Point Load Strength Index and Correlation With Destructive and Nondestructive Tests
Bibliographic record
Abstract
Abstract Multi scale projects of civil, mining, and oil and gas drilling require to determine rock strength as main property of surface and subsurface rocks for safe structures, economic mining operations, as well as drilling performance optimizations. The Unconfined Compressive Strength (UCS) as a main rock strength test is considered a time consuming for sample preparation, costly, and unaffordable. However, it is required for correlations for rock strength obtained from other tests. A semi-Point Load Strength Index (Semi-PLSI) is proposed in this research as a new approach to estimate rock strength. The objectives of this research include reducing testing costs, simplifying sample preparation and testing procedure, precisely localizing, stabilizing, and balancing specimens while testing, and improving accuracy and repeatability. For this test, various sample dimensions and length to diameter ratios are prepared from granite rock type. A 400 (kn) Geomechanics Frame is used for testing and an advanced data acquisition system is utilized for data recording. Data obtained from Semi-PLSI are correlated with data obtained from two main strength types including UCS that represent the shear fracture strength and indirect tensile strength (IT) that represent the tensile fracture strength. Data correlation also includes measurements of ultrasound primary and secondary wave velocities that represents nondestructive data measurements. The good correlation agreements and the verity ways of estimating the strength enrich this new approach of the Semi-PLSI proposed for UCS estimation and pave the road of more 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".