Eco-Efficient Fiber-Reinforced Preplaced Recycled Aggregate Concrete under Impact Loading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.010 | 0.001 |
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 it