Effect of Microwave Heating On The Mechanical Properties and Energy Dissipation Characteristics of Hard Rock
Bibliographic record
Abstract
Abstract To study the effect of microwave on the weakening mechanism of hard rock, uniaxial compression tests were conducted on granite samples after microwave treatment, and an acoustic emission system was established to monitor the fracture evolution in such rock samples. The effects of microwave irradiation on the stress-strain curve, acoustic emission characteristics, and energy dissipation characteristics of granite were investigated in the experiment. The results show that: 1) With the continuous increase of microwave irradiation power and time, the compaction stage and crack development stage of the sample gradually increase, and the elastic stage gradually becomes shorter; the trend of post-peak stress drop becomes slower, changing from “cliff type” to multi-step type “. The failure of the sample indicates that they become less brittle and more ductile; 2) Microwave irradiation reduces the strength. The overall acoustic emission count rate is weak before the peak and increases rapidly near the peak. The count rate has also changed from sparse to dense, and with the increase in the irradiation power and time, the total acoustic emission count rate increases; 3) From the energy point of view, the weakening of granite by microwave irradiation will not only decrease the energy-storage limit of granite and increase the proportion of dissipated energy in the failure process, but also decrease the rate of release of elastic energy after the peak. Therefore, microwave irradiation will not only reduce the mechanical energy dissipated during damage, but also reduce the intensity of potential dynamic disasters.
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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 | 0.000 | 0.001 |
| 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 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".