Leeb Hardness Testing for Rock Strength Estimation: A Case Study from the Bingham Canyon Mine
Bibliographic record
Abstract
ABSTRACT: Understanding rock strength is essential for assessing mine stability. Uniaxial Compressive Strength (UCS) is a critical measure of rock strength, though the frequency of laboratory testing is typically limited due to cost and time constraints. Rock hardness (R-value) is often used as a first order estimate of UCS in mining rock mechanics. R-values are measured using qualitative assessments with the geologic hammer and are easy to collect but inherently subjective. Additionally, Point Load Testing (PLT) provides a strength index that can be correlated with UCS values, but the test is destructive, and can be time-consuming. Originally developed for the metals industry, the Leeb Hardness (LH) tester offers a rapid, quantitative, safer, hand-held and non-destructive alternative for measuring rock hardness. While LH data will not replace the need for UCS testing, the high-density nature of LH data also makes it ideal for predictive modeling and as a reliable input for 3D geotechnical models. This study utilizes drill core data from the Bingham Canyon Mine and focuses on establishing a correlation between LH values, traditional hardness estimates (R-values), and PLT data using linear regression and correlation coefficient analyses for each rock type. Analyzing the Quartzite (QZ) and Monzonite (MZ) data, LH showed moderate correlations with PLT. Trends in data infer PLT values underestimate strength compared to LH, however limitations for the LH tester are also explored and discussed in this study. Future work will focus on developing the LH-UCS correlation for the specific lithologies present at Bingham Canyon, enabling more accurate geotechnical characterizations and informing stability assessments for ongoing and future mining operations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".