Elucidating Chemo-Mechanical Synthesis and Microstructural Study on the Performance of Partial Cement-Based Concrete Composites Against Sulphate Attack – A Review
Bibliographic record
Abstract
The well-known weakness of cement concrete against external/internal sulphate attack and an estimated 7-10% global greenhouse gas emission by the construction industry (mainly contributed by cement manufacturing and supply have encouraged researchers to elucidate the chemical synthesis taking place in the preparation and hydration of cement concrete along with the factors affecting the sustainability of hardened concrete. In this review study, an endeavour has been made to explore the use of Supplementary Cementitious Materials (SCMs) of different hydrocarbon compositions, including organic/ inorganic compounds like pozzolans derived from natural (zeolite/ metakaolin derived from kaolinite), agricultural (rice husk ash, corn cob ash) and industrial fields Pulverised Fly Ash (PFA), Silica Fume (SF) and a renowned cement replacement material, i.e., Ground Granulated Blast Furnace Slag (GGBS),). The partial replacement of 0-30% pozzolans with cement as a binder has been reviewed objectively to achieve economic/ environmental benefits by enhancing strength and durability against dangerous sulphate attacks. The chemo-mechanical synthesis involving SCMs has been explored to understand the formation of additional calcium silicate hydrate C-S-H gel by blending various pozzolans. The research elucidates an improvement in strength up to optimum ratios of 1-15% for different SCMs. However, the strength was observed to reduce beyond a certain % ratio of SCMs blending due to the formation of expansive alkaline silica hydroxide gel, which causes cracking and weak structure. The aviation industry is considered the top emitter of CO2 (3% of total global emissions), however, the construction industry emits 7-10% of global greenhouse gases, which is nearly three times greater. Therefore, the supportive use of up to 90% SCMs can result in a significant reduction of CO2 by the construction industry based on the type/ratio of blending SCMs. Microstructural studies using scanning electron microscopy SEM and X-ray Diffraction (XRD) have also been explored. These microstructural studies have further clarified the development of ettringite in concrete after sulphate attack and the beneficial use of pozzolans to a certain extent to prevent the formation/ propagation of ettringite-specific cracks in the micro/ nano-pores of concrete structures. In general, research has shown that the addition of SCMs in concrete results in an increase in strength and superior resistance to sulphate attack.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".