Performance Evaluation of Different Insulating Materials using Field Temperature and Moisture Data
Bibliographic record
Abstract
Including insulation layers in pavement structures has become a common strategy to minimize frost penetration in cold regions. This study investigated the performance of two different insulation materials, extruded polystyrene board and bottom ash, in a test road in Edmonton, Alberta, Canada, eight years after construction. The two insulation materials were used in a fully instrumented test road, including three insulated sections 20 m in length. The insulated sections are as follows: the first section has 1 m of bottom ash (B. Ash), the second section has a 10 cm polystyrene layer (Poly-10), and the third section has a 5 cm polystyrene layer (Poly-5). Both B. Ash and polystyrene layers were placed on top of the subgrade layer, at a depth of 70 cm from the surface. A conventional section next to these three sections was used as the control section. Volumetric water content data and temperature variation were used to analyze the influence of the insulation materials on the subgrade. It was concluded that both B. Ash and Poly-10 layers protected the subgrade from freezing. The Poly-10 section showed the lowest rate of change in subgrade temperature during the monitoring period. B. Ash and Poly-10 reduced the frost depth by 23% and 70% compared with the control section, respectively. It was concluded that Poly-10 protected the subgrade soil from freezing and excessive moisture more effectively than B. Ash; however, the temperature in the layer above the insulation layers (pavement base layer) was significantly lower during winter for the Poly-10 section.
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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".