Direct tensile failure of cementitiously stabilized crushed rock materials
Bibliographic record
Abstract
Tensile behaviour plays a very significant role in the performance of cement-stabilized pavements under traffic, as well as under environmental loading. This paper reports the results of direct tensile strength tests undertaken using new equipment. The tests were performed on specimens of crushed basaltic rock stabilized with three binders, namely, general purpose Portland (GP) cement, general blended (GB) cement, and alkali activated slag (AAS), with application rates of 2%, 3%, and 4% by dry weight. Some tests were conducted by adding 6% and 15% highly plastic clay to crushed basaltic rock. The tests showed that while the tensile strength increased with curing time, the failure tensile strain decreased. The ratio of unconfined compressive strength to tensile strength decreased with curing time, but it stabilized within the range 8–12 after about 7 days of curing. For AAS and GB cement, the failure tensile strain decreased with curing time, stabilizing at about 50 microstrains after 7 days of curing, whereas for GP cement, the failure tensile strain did not change significantly during curing, displaying a value around 40 microstrains. The test results also indicated that the presence of reactive fine-grained soil may have had a significant adverse effect on the potential for cracking in the stabilized pavement materials.Key words: pavement materials, cement stabilization, tensile strength, cracking potential.
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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) | 0.000 | 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 | 0.000 |
| 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".