Study on Influence of Seasonal Freeze-thaw Environment on Crack Evolution of Expansive Soil in Subgrade Based on Genetic Algorithm
Bibliographic record
Abstract
In the cold seasonal frozen soil area, expansive soil is in the environment of alternating wet and dry and freezing and thawing cycle for a long time, so it is easy to form cracks, which greatly impacts its strength, permeability and deformation characteristics. Usually, the frost heave deformation of subgrade is unevenly distributed along the length of the road, so the smoothness of the road surface is poor, the flexible road surface is prone to bulge and crack, and the rigid road surface is prone to break, which affects the normal use of the road. In view of this situation, this paper discusses the influence of seasonal freeze-thaw environment on crack evolution of subgrade expansive soil based on GA(genetic algorithm). By studying the crack volume fraction distribution under wet-dry and wet-dry freeze-thaw coupling cycles, it is found that the crack volume fraction of the sample under the first coupling cycle is 6.09%, and it gradually stabilizes to 10.6% after the 15th cycle, which is about 1.24 times that of the wet-dry cycle. It can be seen that GA can effectively improve the crack evolution of subgrade expansive soil caused by seasonal freezing and thawing environment. Based on the different frost heaving rates of fill in seasonal frozen soil area of GA, this paper analyzes the distribution and evolution of subgrade deformation and stress during freeze-thaw cycle, and further discusses its influencing factors, revealing the occurrence mechanism of subgrade diseases in depth.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".