Monitoring of Flexible Pavement Structures during Freezing and Thawing
Bibliographic record
Abstract
The structural behavior of flexible pavement in cold regions is greatly affected by environmental factors and traffic loads. The main objective of this study was to better understand the response of pavement structures during thawing and to better predict the loss and recovery of the bearing capacity as a function of the evolution of the thaw and temperature in the pavement. Two identical test sections were used; one was built in the geotechnical laboratory of Laval University and one was located at the Laval University Road Experimental Site (SERUL). Each layer of the tested sections was instrumented with strain, stress, moisture, and temperature sensors. In the laboratory, a heavy vehicle and environmental simulator, which can control the air temperature on the pavement surface, was used to apply real traffic loads and control temperature. A temperature of −10°C was used for freezing, and a temperature of 10°C was used for thawing. The carriage speed was set a 5 km/h, the tire pressure was set at 700 kPa, and loads of 5000, 5500, and 4000 kg were tested to simulate standard, winter premium, and spring load restriction conditions. At the SERUL, a falling weight deflectometer (FWD) was used to simulate heavy loads. The results indicate that the freezing of a given structural layer reduces the strains and stresses in the layer, as well as in the underlying layers. After one freeze-thaw cycle, the reduced moduli in unbound materials indicate that some damage occurs during freezing and thawing. Also, strain increases significantly when the thaw front penetrates into the pavement and at the beginning of summer when the rise of temperature reduces the stiffness of the asphalt concrete layer. Finally, we noticed that, during freezing, a 10% load increase induces a 10% increase of strain and stress in the pavement structure.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".