THE ESTIMATION OF DAMAGE STATUS AND FRAGMENT SIZE DISTRIBUTION FOR MINING AND TUNNELING APPLICATIONS
Bibliographic record
Abstract
THE ESTIMATION OF DAMAGE STATUS AND FRAGMENT SIZE DISTRIBUTION FOR MINING AND TUNNELING APPLICATIONS Abstract Rock failure widely exists in geotechnical engineering, particularly in tunneling and underground mining. Accurate estimation of fragment size distribution not only can ensure the safety and efficiency of engineering projects but is also helpful to save on transportation expenses and avoid costs caused by secondary fragmentation. This research proposes a method to estimate the size distribution of rock fragmentation based on the self-similarity. In this paper, a combined use of fractal theory, elasto-plastic theory and energy conservation theory was adopted. By considering damage energy and size distribution, the fractal damage constitutive model is proposed. In this model, fragment size, damage state and fractal dimension are three main influencing factors. To verify this model, red sandstone was selected as a case study. By fitting the stress-strain curves and quantity-frequency curves, the brittle index and fractal dimension were calculated. Through utilizing the method proposed in this research, the damage status and fragment size of jointed rock mass and collapsed roof in goaf can be estimated. Eventually, implementation of the estimator model would support the attempts towards autonomous operations and vision-based monitoring approaches. Keywords: Fractal theory; Damage constitutive model; Size distribution; Rock fragmentation
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".