Experimental and Numerical Study on Response Characteristics of Airport Pavement Subjected to Wetting in Silt Subgrade
Bibliographic record
Abstract
Silt is often selected as the filling soil in Northwest and North China. “Pot cover effect” or rainfall infiltration are easily to cause localized wetting during the service period of airport. The response characteristics of pavement derived from aircraft loading under wetting condition are the basis to explore the evolution law of pavement damage. To investigate this response characteristics for different parameters of pavement under wetting condition, a self-developed model test system was developed, and the verified numerical model was then established. The interaction mechanisms of pavement slabs, and the influence of wetting on mechanical response in loading area of pavement under different pavement parameters were analyzed. The results showed that the increased vertical deformation depends on water-holding capacity of silt, and the vertical deformation under loading is little affected by pavement parameters. The peak shear stress and the increased peak shear stress derived from wetting at the bottom of pavement layers are largely influenced by elastic modulus of base course. For cement concrete pavement, the increase in load transfer capacity of joints cannot decrease the increase of peak stress, including horizontal stress and shear stress, derived from wetting. The increase in load transfer capacity of joints can decrease the stress concentration in surface layer, while the elastic modulus of surface layer has little influence. For bituminous pavement, optimal parameters of base course can be obtained to decrease the peak stress and the increase of peak stress derived from wetting in subgrade. The research can provide technical basis for the structure design, optimization, and disease treatment of pavement in airport engineering.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".