Municipal solid waste incineration (MSWI) bottom ash-blended cementitious materials: Performance, challenges, and potential solutions
Bibliographic record
Abstract
The recycling of municipal solid waste incineration (MSWI) bottom ash as a supplementary cementitious material (SCM) has attracted global attention, driven by the increasing availability of this by-product and the demand for sustainable SCMs to lower CO2 emissions from cement production. Currently, the widespread use of MSWI bottom ash in the cement industry is hindered by the lack of guidelines to regulate material composition, optimize pretreatment processes, and specify mix design requirements. This review compiles and analyzes literature data on mix design, microstructural evolution, fresh properties, mechanical properties, durability, leaching risks, and environmental impacts of MSWI bottom ash-blended cement pastes, mortars, and concretes. The analysis aims to assess the influence of the pretreatment and physicochemical properties of bottom ashFootnote1 on the microstructure and performance of blended cementitious materials.Footnote2 The Ash Impact Strength Index (AISI) is introduced to quantify the effects of various factors on compressive strength, enabling direct comparison across different studies. Based on the statistical analysis of the 28-day AISI, the key quality requirements for MSWI bottom ash as an SCM are proposed, along with the optimal mix design. This work provides valuable insights and practical guidance to support the integration of bottom ash into the cement industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".