Probabilistic Approach for Q-based Ground Support Design
Bibliographic record
Abstract
For tunnel, cavern, and shaft design, the inherent variability in a given rock mass domain makes accurately estimating rock mass quality and support requirements difficult. To capture the variability in rock mass properties when using the Q system, a methodology incorporating a statistical analysis of measured Q input parameters and Monte Carlo Simulation was developed to perform a probabilistic ground support design approach. Probability and cumulative density function curves were then developed using the mathematical program MATLAB to guide in estimating ground support based on all potential rock mass conditions. To illustrate the proposed approach, two hypothetical tunnels were designed based on real data from two previous projects. Finite Element Modelling was used to evaluate the suggested Q rock support performance in a range of rock conditions for one of the hypothetical excavations to validate the proposed approach. This method demonstrated that associating a range of potential ground conditions instead of a single deterministic value for each input parameter can provide a quantifiable measurement of uncertainty within a given rock mass domain. Additionally, the approach provides insight into the design criteria for ground support in underground excavations to potentially reduce overly conservative and costly recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".