An Airfield Soil Pavement Design Method Based on Rut Depth and Cumulative Fatigue
Bibliographic record
Abstract
The structure and damage modes of soil pavement, as well as existing problems in current design methods, were comprehensively analyzed, and a new design method for airfield soil pavement was proposed. The proposed method avoids the use of the “designed aircraft” concept and instead adopts the cumulative fatigue theory widely used in permanent airfield design at present. Moreover, in view of the lack of aircraft wheel trajectory distribution data, an approximate method for calculating the wheel trajectory distribution considering the side slip distance of the aircraft was proposed and the equivalent width of the wheel tread was calculated by introducing the modulus ratio. Finally, the pass-to-coverage ratio was obtained. According to the characteristics and damage modes of airfield soil pavement, rut depth was determined to be the unique factor affecting soil pavement damage, and resilient modulus was used as the control variable to improve the adverse impact of the empirical method. Furthermore, according to the rut prediction formula for airfield soil pavement put forward by the US Army Engineer Research and Development Center, a fatigue equation based on the resilient modulus was proposed to calculate the allowable number of repetitions. To verify the reliability of the design method, a test section was constructed at a test center in Jining, China, and the theoretical maximum allowable repetitions on the soil runway were calculated by the currently used California bearing ratio test, the β-fatigue equation, and the proposed method. Aircraft traffic tests were carried out on the test section. Finally, the theoretical and test results were compared and the values calculated via the proposed method were found to be consistent with experimental values, thereby validating the reliability of the method.
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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".