Effect of using wastewater from the ready-mixed concrete plant on the performance of one-part alkali-activated GBFS/FA composites: Fresh, mechanical and durability properties
Bibliographic record
Abstract
Water scarcity is the world's most pressing issue, as concrete batching facilities and concrete mixer trucks produce massive amounts of wash water every day. Recycling waste water from ready-mix concrete factories' concrete washing water is critical for conserving hundreds of millions of tons of water and preventing water and soil contamination. This study examined the impact of waste washing water on the microstructural, durability, fresh, and mechanical characteristics of one-part alkali-activated ground blast furnace slag (GBFS)/fly as (FA) composites (AAC) containing partial and complete replacement of tap water under ambient conditions. GBFS was used as the main binder in the production of AAC. FA was also used as a binder at 0%, 25%, and 50% instead of GBFS. Sodium metasilicate (MS) was used as a one-part activator at two dosages 7.5% and 15% of the total binder. The fresh properties (setting time and flowability), physical properties, compressive and flexural strength (3, 7, 28, 90, and 180 days) and durability (high-temperature resistance, freeze, and thaw resistance , drying shrinkage , sorptivity , HCl and MgSO 4 resistances, NaCl effect and alkali-silica reaction) and microstructure analysis were investigated. The findings showed that the use of wastewater (WW) instead of tap water (TW) contributed positively or had no serious negative effect on the mechanical and durability properties of AAC. Compressive strength of 72.37 MPa and 81.67 MPa was gained with the inclusion of 50%WW at 7.5 and 15 %MS content respectively. The findings showed that WW improved the workability of fresh ACC containing FA, reduced dry shrinkage and sorptivity of ACC with 15%MS content, and refined the pores of hardened ACC. The results also supported that WW contributed to the decrease in expansion due to ASR and sulfate expansion. Using WW improved the high temperature and F-T resistance of ACC mixtures containing 15%MS content.
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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".