Concrete Coefficient of Thermal Expansion (CTE) and Its Significance in Mechanistic-Empirical Pavement Design
Bibliographic record
Abstract
Agencies using the Mechanistic-Empirical Pavement Design Guide (MEPDG) and its associated software application (DARWin-ME) are encouraged to undertake local validation and calibration efforts. This local calibration effort would serve to improve the transfer functions that take the MEPDG stress and strain outputs and translate them to pavement performance indicators such as slab faulting, fatigue cracking and pavement roughness. The coefficient of thermal expansion (CTE) is one of the critical factors considered in the design of concrete pavements. As this factor is rarely specified on Canadian projects, pavement designers typically rely on the MEPDG default values or an average value rather than project specific values. While this tended to produce reasonable results when using empirical pavement design procedures, the CTE has a much larger impact on the pavement design when using the much more comprehensive MEPDG procedures. DARWin-ME takes advantage of the advances in material mechanics, axle-load spectra and climate data for predicting pavement performance. Relying on the default values of CTE may lead to flawed assumptions about the pavement's thermal response and potential distress. This paper discusses the importance of project specific values for CTE and provides resources for reliable data. This paper also discusses the FHWA's standard test method (adopted by AASHTO as TP60-00) for determining the CTE of concrete pavements. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".