Impact of load frequency on the laboratory transfer function for subgrade soil rutting behavior
Bibliographic record
Abstract
Subgrade soil performance and flexible pavement system responses are significantly influenced by loading parameters and environmental factors. The structural rutting in subgrades is especially important, as inadequate permanent strain rates may cause drainage issues that require costly rehabilitation. Unpaved roads are generally located in remote areas and characterized by heavy vehicles, exacerbating this problem. This study emphasizes how crucial load parameters—like amplitude and frequency—impact the accumulation of permanent strain under cyclic loading for different pavement subgrade soils. The research offers comprehensive insights into the behavior and interaction of two distinct subgrade materials, clay and silty sand, through cyclic triaxial testing under varying stress and moisture conditions. Analysis of the transfer curve reveals that frequency is critical in altering the function form, regardless of soil type, water content, or imposed load size. The findings underscore that frequency, more than any other factor, significantly impacts the behavior and characteristics of the pavement structure, making it a key parameter in understanding and predicting structure responses. Furthermore, for a maximum allowable resilient strain, the number of cycles may vary up to 20 times for frequencies ranging from 0.3 Hz to 10 Hz. This implies that damage can be accelerated by fewer heavy vehicle passes, especially when the road condition forces the speed to moderate speeds (low frequencies). Assessing the soil stability and rutting potential in situations involving large trucks travelling at slow speeds while carrying heavy loads is crucial. Designers should thus modify their damage criteria to account for these circumstances.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".