Energy evolution and damage constitutive model of rock-like joint surfaces under wet-dry cycles
Bibliographic record
Abstract
• The evolutionary characteristics of energy parameters in joint surface under wet-dry cycles is examined based on an improved energy model. • Plastic energy and fracture energy are collectively defined as damage energy, which is subsequently employed to characterize the shear damage variable • A shear damage constitutive model was established incorporating wet-dry cycles and loading coupling effects. To investigate the deterioration mechanism of joint surfaces under wet–dry cycles in reservoir fluctuation zones, artificial joint samples with three representative roughness levels were prepared and subjected to direct shear testing after different numbers of wet–dry cycles. A simplified energy calculation model incorporating post-peak characteristics was improved, along with a damage constitutive model that couples wet-dry cycles with loading. Key findings include: (1) Wet-dry cycle causes opposing energy trends - decreasing in high-roughness joints (J5, J10) but increasing in low-roughness joints (J1), with pronounced elastic-plastic energy conversion in rough joints. Post-peak behavior shows asperity capacity reduction, except for brittle energy rebound in dry J1 samples. (2) damage variable equation under shear loading was formulated using Logistic function to characterize joint surface damage via energy evolution, demonstrating excellent fitting accuracy ( R ² > 0.97). By integrating the damage variable equation with damage equations under wet-dry cycles, a coupling damage model was derived. The high consistency between model and test curves validated the model’s reliability (relative standard deviation RSD < 10%). (3) Parametric analysis via the control variable method revealed that parameters a and r jointly govern damage curve evolution, while parameter i N , τ r N a , and r collectively influence constitutive curves. Wet-dry cycles and JRC directly affect the parameter i N and τ r N , subsequently altering the damage energy and ultimately achieving indirect regulation of parameters a and r . These results provides a theoretical reference for stability analysis of reservoir slopes and related engineering applications, such as similar model tests.
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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) | 0.000 | 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".