Estimating Thermal Conductivity of Pavement Granular Materials and Subgrade Soils
Bibliographic record
Abstract
The thermal conductivity of soils and granular materials is one of the most important parameters for frost action and thermal analyses in pavements and civil engineering infrastructures. Many predictive models were developed in past decades to correlate thermal conductivity of soils to basic soil index properties. Recently, relationships that cover the complete range of saturation in unfrozen and frozen states were established to consider the effects of porosity, geologic origin and mineralogy, soil type, particle type, and unfrozen water for a variety of soils. A simple and practical tool was developed to estimate thermal conductivity of pavement granular materials and subgrade soils directly as a function of the degree of saturation (water content) on the basis of these generalized thermal conductivity relationships. A new thermal conductivity equation is proposed to estimate thermal conductivity functions for soils such as well-graded gravels; coarse, medium, and fine sands; silty and clayey soils, and peat in unfrozen and frozen states. The new equation was successfully used to predict thermal conductivity functions of four different types of unfrozen and frozen soils from the literature (gravel, sand, clay, and peat). A set of charts is proposed to assess thermal conductivity of soils readily. The use of these charts is illustrated with examples. Both numerical and graphical estimating methods proved to provide accurate prediction results for thermal conductivities for soils and construction materials.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".