Evaluation of the physical and mechanical properties of concrete with steel fibers from recycled tires for applications in coastal areas
Bibliographic record
Abstract
This article addresses the low tensile strength in reinforced concrete structures affected by corrosion of reinforcing steel, as corrosion weakens the concrete by generating stresses that exceed its strength.Structures in coastal areas, such as Lima, are especially vulnerable due to the presence of chlorides and extreme weather conditions, which increase atmospheric corrosivity.The objective of this study is to evaluate the physical and mechanical properties of concrete enhanced with steel fibers obtained from recycled tires (RTSF) for applications in coastal areas.The characterization of the aggregates and the RTSF was carried out.In addition, a concrete mix design was developed with the addition of, 20kg/m3, 30kg/m3 and 40kg/m3 of RTSF.In addition, tests were carried out on the fresh concrete (slump, temperature and air content) and tests on the hardened concrete (tensile, compressive and flexural strength) to determine the workability, thermal control and the amount of air trapped in the mix, key factors for the durability and quality of the concrete.The analysis of the properties of RTSF shows that its incorporation improves various characteristics of the concrete, such as compressive and tensile strength, especially with 40 kg/m³ of fibers, which increase these strengths by 6.97% and 6.65%, respectively.An increase in the modulus of rupture is also observed, with the greatest increase in the 30 kg/m³ mix.However, the incorporation of RTSF increases the water absorption and porosity of the concrete, which can be a factor to consider in humid environments.
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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".