Influence of paste/mortar rheology on the flow characteristics of high-volume fly ash self-consolidating concrete
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
Self-consolidating concrete (SCC) is known for its excellent deformability, high resistance to segregation and use without applying vibration in congested reinforced concrete structures characterised by difficult casting conditions. The rheology of paste and mortar is important to understand the flow behaviour of SCC. The current paper presents the results of a comprehensive investigation to establish correlations among the rheological parameters of paste/mortar (such as yield stress and plastic viscosity) and the fresh properties of concrete mixtures incorporating high volumes of fly ash (FA) (such as slump flow, flow time, filling capacity and segregation index) in order to predict the flow behaviour of corresponding SCC. The specific Marsh cone flow time of paste and fresh properties of concrete were also correlated. Twenty-one mixtures of paste, mortar, and concrete with four variables such as total binder content (350–450 kg/m 3 ), percentage of FA as cement replacement (30–60% by mass), percentage of superplasticiser (SP) (0·1 to 0·6% by solid mass), and water-to-binder ratio (w/b) (0·33–0·45) were investigated. An attempt had been made to identify the range of rheological parameters of paste and mortar to achieve desired fresh properties such as flowability and self-consolidation of corresponding SCC. The prediction of the rheological parameters of paste/mortar is relatively easy compared with the prediction of the fresh properties of SCC. Establishment of correlations among the rheological parameters of paste/mortar and fresh properties of SCC can save time and energy associated with the mix proportioning of a satisfactory FA SCC.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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