Impact of load frequency on the laboratory transfer function for subgrade soil rutting behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 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) | 0.000 | 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 it