Reliability of strength estimates based on limited laboratory data
Bibliographic record
Abstract
In an open pit mine operation, the quality and quantity of collected geomechanical data can have significant implications in the design of safe and economically viable slope designs. The selection of representative values of the rock mass properties is not straightforward, given the inherent geological and structural variability within an orebody. Under these circumstances the design of a comprehensive geomechanical sampling program is critical. Such a program, however, has to comply with practical and financial constraints while developing a degree of confidence in the quality of the geomechanical data. A common target level of confidence in the rock mass properties used in slope design is higher than 80% for an open pit mine at the operations stage. This requires an ongoing maintenance of the geomechanical database and model. In practice, given the perceived high costs of laboratory testing, quite often only a relatively small number of samples are selected for laboratory testing. This leads to a series of questions pertaining to the confidence level that can be assigned to values obtained by testing only a few samples. This paper investigates the potential of small-sampling theory to provide practical recommendations on the adequacy of a testing program. The geomechanical database of an Anglo American operating open pit mine was reviewed with respect to the strength properties obtained through a series of ISRM suggested testing methods. For the purposes of this investigation the focus was on uniaxial compressive strength (UCS) results but the methodology can be applied to other material properties. In this case study, the mine geological model identified six distinct rock types. Strength values for six different rock domains were analysed using the confidence interval approach. In order to investigate the sequence of testing on the interpretation of results, statistical analyses were also performed by randomly interchanging the order of test results for each rock domain. The results showed that even if the number of specimens tested is higher than the minimum proposed by the International Society for Rock Mechanics (ISRM) suggested methods, the sample size was too small to obtain a reliable strength value for most of the rock domains. Furthermore, the results showed that the minimum sample size obtained using the confidence interval approach is significantly influenced by the test results sequence used for the analyses. Based on the results of this study, there is a demonstrated need for a method to determine the minimum sample size while minimising the influence of the testing sequence.
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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 | 0.049 | 0.218 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".