Evaluation of Compressive Strength of Asphalt Mixture from Marshall Stability and Indirect Tensile Strength
Bibliographic record
Abstract
This research includes a laboratory study that deals with the evaluation of compressive strength for three different asphalt mixtures of hot mix asphalt (HMA).First, traditional HMA was prepared, and then, two asphalt mixtures were prepared using two different polymers, namely styrene-butadiene-styrene (SBS) using 5% SBS and reactive ethylene-butyl acrylate-glycidyl methacrylate terpolymer (Ax) using three concentrations (1% Ax, 2% Ax, and 3% Ax).Polymers are used as additives to enhance the performance of mixtures by enabling the creation of blends that resist rutting and cracking.The polymer-asphalt blends and conventional mixes were assessed by examining their Marshall stability, indirect tensile strength, and compressive strength.The results indicated that the mixtures modified with Ax exhibited enhanced mechanical properties, followed by the modified blend with 5% SBS.The modified asphalt mixtures exhibit superior performance compared with unmodified asphalt, as they possess greater Marshall stability, indirect tensile strength, and compressive strength.The relationships were derived for all three asphalt mixtures (traditional HMA and the two polymer-modified mixtures).These relations were quite strong, as indicated by the high value of R 2 .The empirical relations can be used to predict compressive strength in a mix based on stability and indirect tensile strength data without performing the compressive strength test.Moreover, the association between compressive strength and stability proves to be more robust than the correlation between compressive strength and indirect tensile strength.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".