Use of Blast Furnace Dust in the Production of Asphalt Concrete for Pavements, Performance and Environmental Contribution
Bibliographic record
Abstract
This study analyzed the possible use of a residue from the steel industry, blast furnace dust, as aggregate in asphalt mixtures for pavements, as a possible solution to the problems of generation and accumulation of industrial waste in the production of steel, as well as the exploitation of non-renewable materials for infrastructure construction. In the production of steel, solid residues such as slag and blast furnace dust are generated, which become industrial waste. Another found issue in pavement construction is the exploitation and use of stone materials are necessary. Two test states were selected to achieve the established objective where blast furnace dust totally (100%) or partially (50%) replaced the conventional fine aggregate in an asphalt mixture at the laboratory. The applied methodology consisted of four stages: establishing the properties of the materials, determining the composition of the blast furnace dust, designing each of the mixtures using the Ramcodes methodology, and finally performing performance tests such as dynamic modulus and fatigue laws. The results show an acceptable behavior of the blast furnace dust and allow to define that the use of this residue is technically feasible in manufacturing asphalt mixtures for pavements.
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.
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 | 0.001 | 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) | 0.000 | 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".