Study on strength damage model considering resistivity and failure characteristics of the frozen soil–rock mixture under different loading rates
Bibliographic record
Abstract
The strength damage and deformation failure of frozen soil–rock mixture (FSRM) often restrict the safety of the major engineering construction in cold areas or the spatial development of urban underground water-rich rock and soil masses. To investigate the uniaxial strength damage evolution and failure characteristics of FSRM under different loading rates (0.3, 0.6, 3, 6, 30, and 60 mm·min−1) in the quasi-static range, resistivity monitoring and image recognition technology were used to study the time-stress-volumetric strain-resistivity changes. The results indicate that the peak stress, peak strain, initial yield modulus, and tangential modulus of FSRM increase rapidly before increasing slowly as the loading rate increases, and there are critical loading rates and post-peak failure phenomenon. Three distinct types of failure modes, bulge failure, oblique shear failure, and fragmentation failure were observed at low (0.3–0.6 mm·min−1), medium (3–6 mm·min−1), and high loading rates (30–60 mm·min−1), respectively. The macroscopic failure of the FSRM at different loading rates arises from a combination of strain rate hardening of the strength and damage softening of the structure. To predict the stress-strain characteristics at various loading rates, a damage prediction model with a damage variable correction factor considering residual strength was employed based on the improved Duncan–Chang model and damage theory of electrical resistivity, and the predicted results were in good agreement with the experimental data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".