Fresh and Hardened Properties of Engineered Geopolymer Compositewith MgO
Bibliographic record
Abstract
In this paper, the early results of an ongoing investigation on self-healing engineered geopolymer composites (EGC) are presented.The EGC was developed using powder based alkali activators and MgO was added as self-healing agent.Two types of source materials were used to produce EGC.One EGC mix had slag and class C fly ash as source materials and termed as binary mix.The other one had slag, class C fly ash and class F fly ash and termed as ternary mix.Setting time, slump flow, fresh density and rheology were measured as fresh properties of the developed geopolymer composites.As hardened properties, compressive and direct tensile strengths were evaluated.It was observed that addition of MgO delayed the setting time of both the EGC mixes.The rheology of the developed geopolymer mixes complemented the hardened properties of the mixes.It was found that binary geopolymer mix exhibited superior performance as compared to its ternary counterpart due to presence of class C fly ash only that ensured higher amount of CaO.It was also observed that EGC, developed in the present study, experienced strength values (both compressive and direct tensile) that are comparable to the values of the previous studies even with the addition of MgO.Moreover, strain hardening characteristic was observed for both EGC mixes under direct tension test.Hence, it is evident that the initial outcomes of the experimental investigation are quite promising and exhibit the importance of conducting further comprehensive studies in order to develop design guidelines for EGC with self-healing capability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".