Goal 4 Long Life Pavement Rehabilitation Strategies--Rigid: Laboratory Strength, Shrinkage, and Thermal Expansion of Hydraulic Cement Concrete Mixes
Bibliographic record
Abstract
This report presents the results of laboratory work on flexural and compressive strength, free shrinkage, coefficient of thermal expansion, and elastic modulus performed on six concrete mixes. The six concrete mixes are typical of those used, or have been considered for use, for the Caltrans Long Life Pavement Rehabilitation Strategies for rigid pavements (LLPRS-Rigid). This report includes descriptions of the concrete materials, mix designs, test methods, and specimen preparation methods used for the study. It presents flexural (ASTM C 78) and compressive strength data (ASTM C 39), an evaluation of the practice of using compressive strength data to estimate flexural strength data, and conclusions regarding the mixes tested and the use of strength tests for design and construction quality control and assurance. The strength of the concrete determines its ability to withstand stress and stress repetitions without cracking, and greater strength results in greater resistance to cracking. This report also presents shrinkage test data and conclusions regarding the mixes and the use of shrinkage testing. Shrinkage causes stresses in the concrete, in addition to load. Greater xi strength often comes at the cost of greater shrinkage, and mix design is often a balancing act between strength and shrinkage. Free shrinkage was tested on concrete beams (ASTM C157-93) and mortar beams (ASTM C 596-96).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".