Performance Assessment of All-Solid-Waste High-Strength Concrete Prepared from Waste Rock Aggregates
Bibliographic record
Abstract
In order to solve the problems of the large-scale resource utilization of iron ore waste rock, waste rock is used to prepare green building materials, but it needs to be further promoted for use in high-strength concrete. In this study, high-strength concrete was prepared using iron ore waste rock as coarse and fine aggregates combined with solid waste-based cementitious materials. The mechanical and durability properties of washed and unwashed concrete with two types of aggregates were compared, including compressive strength, freeze resistance, chloride ion resistance, carbonation resistance, pore distribution, microstructural characteristics, and environmental and economic benefits. The results indicated that water-washing pretreatment significantly reduced the stone powder content of waste stone aggregate from 14.6% to 4.5%, which had a significant effect on the basic properties of concrete. The compressive strength of concrete with water-washed waste rock aggregate was 61 MPa, 64.9 MPa, and 68.8 MPa at 28, 56, and 360 days, respectively, with long-term stability. The washed aggregate concrete had a porosity of less than 4%, freeze-resistant grade of F200, 28 d electrical flux <500 C, and a carbonation depth of less than 10 mm. The improved performance of the washed aggregate concrete was attributed to the fact that after washing pretreatment, the water absorption of the aggregate was reduced, the cementitious materials were fully hydrated, and the internal microstructure was denser. The high-strength concrete prepared in this study effectively used iron ore waste rock and solid waste-based cementitious materials, which not only reduces environmental burden but also provides basic data references for future engineering applications using iron ore waste rock aggregate concrete.
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How this classification was reachedexpand
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) | 0.001 | 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) | 0.002 | 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 teacher head, 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".