Application of Stiffness Damage Test on Rubberized Concrete
Bibliographic record
Abstract
ABSTRACT Utilizing waste rubber tires in concrete as a partial replacement of aggregate reduces its carbon footprint and modifies its performance. Several researchers have evaluated the properties of crumb rubber concrete (CRC). This article evaluated the potential of one of the most promising testing techniques for concrete (i.e., the stiffness damage test (SDT)) for CRC. Fine crumb rubber aggregate (FCRA) was added as a replacement for fine aggregate at a dosage of 10 % by volume. Results reveal that compressive strength decreased by 29.5 %, 28.4 %, and 28.0 %, and tensile strength decreased by 31.1 %, 31.3 %, and 28.0 % because of FCRA at 28, 56, and 90 days, respectively. A strong relation was found between the strength and time (i.e., specimen age) and was represented linearly with R2 around 0.99 for compressive and tensile strength. The modulus of elasticity of CRC showed 27.7 %, 26.0 %, and 25.5 % reductions at 28, 56, and 90 days, respectively. Poisson’s ratio for the same mixture exhibited an increase of about 34.7 %, 31.3 %, and 28.0 % at 28, 56, and 90 days, respectively. The results showed that SDT output parameters, including the hysteresis area, stiffness, plasticity deformation, and elasticity, decreased as a function of time and were sensitive enough to quantify the change of CRC mixture properties. In addition, adding FCRA caused an increase in plasticity deformation and elasticity over time.
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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 | 0.000 |
| 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".