Recycling Untreated Coal Bottom Ash with Added Value for Mitigating Alkali–Silica Reaction in Concrete: A Sustainable Approach
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
Each year, about 730 million tons of bottom ash is generated in coal fired power plants worldwide. This by-product can be used as partial replacement for Portland cement, favoring resource conservation and sustainability. Substantial research has explored treated and processed coal bottom ash (CBA) for possible use in the construction industry. The present research explores using local untreated and raw CBA in mitigating the alkali–silica reaction (ASR) of reactive aggregates in concrete. Mortar bar specimens incorporating various proportions of untreated CBA were tested in accordance with ASTM C1260 up to 150 days. Strength activity index (SAI) and thermal analysis were used to assess the pozzolanic activity of CBA. Specimens incorporating 20% CBA achieved SAI greater than 75%, indicating pozzolanic activity. Mixtures incorporating CBA had decreased ASR expansion. Incorporating 20% CBA in mixtures yielded 28-day ASR expansion of less than the ASTM C1260 limit value of 0.20%. Scanning electron microscopy depicted ASR induced microcracks in control specimens, while specimens incorporating CBA exhibited no microcracking. Moreover, low calcium-to-silica ratio and reduced alkali content were observed in specimens incorporating CBA owing to alkali dilution and absorption, consequently decreasing ASR expansion. The toxicity characteristics of CBA indicated the presence of heavy metals below the US-EPA limits. Therefore, using local untreated CBA in concrete as partial replacement for Portland cement can be a non-hazardous alternative for reducing the environmental overburden of cement production and CBA disposal, with the added benefit of mitigating ASR expansion and its associated costly damage, leading to sustainable infrastructure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it