Bibliographic record
Abstract
Tensile failure of rocks is a main problem in underground engineering applications, where rocks are subjected to dynamic loadings while under in situ stresses. When disturbed by dynamic loads from blasting or seismicity, underground structures are vulnerable to tensile failure. Therefore, it is of great importance and thus the aim of this thesis to investigate the dynamic tensile failure of rocks subjected to underground in situ stresses, experimentally and theoretically.\nFor the experimental study, the split Hopkinson pressure bar (SHPB) system is modified and developed with a hydraulic press for the dynamic tests under different in situ stress states. The Brazilian disc specimens made from Canadian Laurentian granite are first subjected to a prestress simulating in situ stress (including pre-tension, hydrostatic confinement, and triaxial stress state) and then loaded dynamically using the modified SHPB. For each stress state, several groups of tests are conducted under different levels of prestress and loading rate. The result shows that for all of the tests, the dynamic tensile strength increases with loading rate, revealing the so called rate dependency, which is common for engineering materials. The dependence of the dynamic tensile strength on different prestress conditions are also examined.\nFor the theoretical study, the Dominant Crack Algorithm (DCA) is adopted to model the dynamic tensile failure of rock under different prestress states, with the non-linear regression method Particle Swarm Optimization (PSO). The modeling parameters are obtained using the PSO method based on the experimental results and the DCA modeling is carried out accordingly under the simulated prestress and loading rate. The results demonstrate the applicability of the model as the error is less than 5% with reasonable values of the parameters.\nThese experimental results and the calibrated theoretical model will be of great importance in the design and safety of underground rock engineering projects.
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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) | 0.001 | 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 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".