Comprehensive study on thermal cycling effect on dolomite fracture behavior under three loading modes (I, I/II, II): Experimental and analytical methods
Bibliographic record
Abstract
Daily and seasonal temperature fluctuations significantly influence the behavior of rock masses and their fractures. This study comprehensively examines the impact of thermal cycles on tensile fractures in dolomite, analyzing three loading modes ( I , I/II , and II ) and evaluating the effectiveness of three fracture criteria: stress-based, energy-based, and strain-based. The research involved subjecting samples to heating and cooling cycles between 20 and 60 °C over 50, 100, and 500 cycles, measuring various fracture parameters, including failure envelopes, crack extension angles, fracture stress, and the diameter of the Fracture Process Zone ( FPZ ). The results indicate that the mechanical properties of dolomite initially improved with thermal cycling, peaking between 310 and 368 cycles. The tensile strength increased by 45.5 % after 344 cycles before declining by 7.7 % at 500 cycles. The stress-based maximum tangential stress criterion outperformed energy- and strain-based criteria in predicting fracture parameters, particularly under mixed-mode loading I/II . Generalized fracture criteria provided more accurate predictions of fracture parameters when estimating the FPZ diameter through the fracture toughness ratio ( K IIC /K IC ). Consequently, the FPZ diameter was estimated to decrease from 0.885 mm at zero thermal cycles to 0.804 mm, 0.828 mm, and 0.630 mm after 50, 100, and 500 cycles, respectively. The findings from this study enhance our understanding of how thermal fluctuations and loading conditions affect dolomite fracture behavior, which is crucial for the stability of rock structures in various applications, including geothermal sites, oil and gas reservoirs, tunnels, and landslide-prone areas.
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) | 0.001 | 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".