Insights From Micromechanical Modeling of Intact Rock Failure: Event Characteristics, Stress Drops, and Force Networks
Bibliographic record
Abstract
Abstract We use a bonded‐particle method to investigate event characteristics, b values, internal force distributions, and stress drops in triaxial deformation tests. We simulate brittle through ductile deformation regimes. We find the following: (1) Event rates are proportional to anelastic axial strain: (i) significant and accelerating events rates only occur near peak stress (brittle deformation); (ii) event rates gradually increase until the anelastic strain plateaus (ductile deformation). (2) b value patterns show a systematic decrease when approaching peak stress, after which (i) they increase again (brittle case) or (ii) reach a constant minimum (ductile case). A decrease in b values is indicative of progressive internal damage, with an increasing event rate. (3) Weak‐ and strong‐force networks exist within the sample. Macrofailure of the sample occurs due to collapse of the strong‐force network. Acoustic emissions predominantly occur (i) within the weak‐force network allowing for tensile crack opening and closing despite the large compressive external stresses, and (ii) in areas with the largest spatial force gradients, close to the strong‐force networks. All internal compressive and extensional normal forces display exponential distributions despite uniform boundary stresses. (4) Stress drops of the largest events are inversely proportional to peak stress; the largest stress drops occur for brittle failure, progressively approaching a zero magnitude for ductile deformation. The systemic correlations between b values, the number of acoustic emissions, and stress drops with the maximum principal stress may offer opportunities to invert for changes in the stress state from these remote observables during earthquake cycles.
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) | 0.001 | 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 | 0.001 |
| 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".