Development of green concrete from industrial wastes and carbon dioxide
Bibliographic record
Abstract
This thesis work successfully demonstrated the attainment of a fully waste-derived concrete material through the employment of industrial wastes and carbon dioxide. The aggregate and cement components of this green concrete were artificially synthesized from waste residues originating from the steelmaking and waste-incineration processes, respectively. The selection of these waste materials was based on their convenient abundance, compositional suitability, and as-received fineness. Carbonation was carried out successively through different stages of processing for the purpose of activating strength and converting gaseous CO2 into solid carbonates. Graded angular aggregates were generated from the carbonation of steelmaking slag, which was found to be composed mainly of β and γ di-calcium silicate polymorphs. After optimum processing, the aggregates resembled a very resilient, ceramic-like monolith material. Carbonation of steel slag generated a hardened C-S-H/CaCO3 paste, where C-S-H formed the binding medium while the nano-CaCO3 precipitates acted as the reinforcing composite. Additionally, waste-derived cement was produced from a stoichiometric mix of incinerator fly ash and waste-lime at 1000°C. The main reactive clinker phases generated were chloro-ellestadite and β di-calcium silicate. This binder did not possess hydraulic behaviour but effectively consolidated upon carbonation activation, forming a binding matrix comprised of gypsum, C-S-H, and CaCO3. The final green concrete product obtained from combining the two stable waste-derived components was comparable in performance to commercial benchmark concrete. Concrete so produced consumes no natural resources and can hypothetically sequester up to 12.6 million tons of CO2 per year if the Masonry block industries in United States and Canada were to adopt the prescribed methodologies.
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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 | 0.000 | 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".