Experimental Study on the Deterioration Mechanism of Sandstone under the Condition of Wet-Dry Cycles
Bibliographic record
Abstract
The water content of rock in nature usually changes, because of the rainfall (Wet) and drought (Dry). This wet-dry cycle is one of the most important reasons leading to the deterioration of rock mechanical properties. In the paper, uniaxial compressive strength (UCS) tests of sandstone suffered different wet-dry cycles were conducted, and the ion content in the soaking solution was measured. In addition, the microstructure changes of the sandstone specimens were observed by scanning electron microscopy (SEM), and the pore changes were tested by nuclear magnetic resonance (NMR) technology. The UCS of sandstone decreases by 4.58% after 1 dry-wet cycle, and 18.35% after 20 dry-wet cycles. There are some ions in the soaking aqueous solution after each wet-dry cycle, such as Na+, Mg2+, K+, and Ca2+, etc, and the ion concentration decreases gradually with the increase of wet-dry cycles. The results of SEM and NMR show that the porosity of the sample increases from 10.22% to 12.30% after 20 wet-dry cycles. Based on recorded change of porosity, the damage evolution equation of sandstone is established. As the damage accumulates, the UCS of the specimen gradually decreases. The loss of some minerals and cyclic expansion-shrinkage of clay minerals during the wet-dry cycles are the main reasons for the damage of sandstone microstructure, which eventually results in the deterioration of mechanical properties.
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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 | 0.000 |
| 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) | 0.002 | 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".