Eco-Efficient Fiber-Reinforced Preplaced Recycled Aggregate Concrete under Impact Loading
Bibliographic record
Abstract
This study explores highly eco-efficient preplaced aggregate concrete mixtures having superior tensile characteristics and impact resistance developed for pavement and infrastructure applications. A fully recycled granular skeleton consisting of recycled concrete aggregate and recycled tire rubber granules, and steel wire fibers from scrap tires are first placed in the formwork, then injected with a flowable grout. Considering its very high recycled content and limited mixing and placement energy (only the grout is mixed, and no mechanical vibration is needed), this material has exceptional sustainability features and offers superior time and cost savings. Moreover, typical problems of rapid loss of workability due to the high-water absorption of recycled aggregates and the floating of lightweight tire rubber granules are prevented since the aggregates are preplaced in the formwork. The much higher granular content and its denser skeleton reduce the cementitious dosage substantially and provide high volume stability against shrinkage and thermal strains. The behavior under impact loading of this sustainable preplaced recycled aggregate concrete, incorporating randomly dispersed steel wire fibers retrieved from scrap tires, was investigated using a drop weight impact test. The results show that recycled tire steel wire fibers significantly enhanced the tensile and impact properties. A two-parameter Weibull distribution provided an accurate prediction of the impact failure strength of the preplaced recycled aggregate concrete mixtures, allowing to avert additional costly laboratory experiments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".