Probability assessment of pit wall stability in jointed rock mass
Bibliographic record
Abstract
Based on the real data from engineering–geological investigation and laboratory tests, probability assessment of pit wall stability was performed for pit wall at the Kumtor gold ore deposit in the Kyrgyz Republic. The sensitivity analysis was carried out determine influence of random variables on the pit wall stability based on the computational experiment according to the scheme of the central composite plan. The chosen variable factors were the parameters of the Hoek–Brown criteria, the general angle of the pit wall slope and the ground water level in the pit. The computational experiment was implemented out using the test example of pit wall 400 m heigh. According to the sensitivity analysis, the dominant factor is GSI—geological strength index, which describes how heavily rock mass is jointed and is determined by the results of engineering–geological investigation. With regard to the influence on the pitwall stability, the GSI parameter is twice as high as the compressive strength of the undisturbed sample and three times as high as the general slope angle and ground water level. Thus, the distribution law of pitwall FoS is primarily affected by the distribution law of GSI parameter, which, gentically is individual per each pit wall area. For probability assessment of stability, a random variable is selected to be FoS. The reliability of FoS calculation is based on a FLAC program option to perform complex processing of random variables by the Monte Carlo method and to calculate stability of each implemented variant. In total, 100 variants were implementede. The statistical estimation of the mathematical expectation accuracy of FoS was performed by the sample mean FoS based on the determination of confidence intervals with a given reliability γ=0.99. In our case, testing the hypothesis of the normal distribution law for FoS was rejected in favor of the Weibull distribution. The law of distribution of the GSI parameter in each geological zone was different from normal. The calculated probability of failure by the Weibull distribution was Р(FoS<1)=31.7 %. The decision on the stability of the pit wall was made based on the comparison of Р(FoS<1) with the critical probability of pit wall failure Рcrit. We believe that the most acceptable values of Рcrit are withn the range from 5% to 0%. The comparison of Р(FoS<1) with Рcrit, in our case, indicates the need to carry out measures aimed to increase overall stability of the pit wall in the study section. The reli ability of probability assessment of pit wall stability is based on the implementation of the rules and methods of statistical processing and analysis of both source and calculated data on the specialized program Statistica.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".