Development of statistical models to simulate and optimize self-consolidating concrete mixes incorporating high volumes of fly ash
Bibliographic record
Abstract
Self-consolidating concrete (SCC), a latest version of high performance concrete, has created tremendous interest today as it can be easily placed in congested reinforced concrete structures with difficult casting conditions. It also reduces the construction time and cost of the labor. Normally SCC is being developed using a superplasticizer to generate desired flow and a viscosity modifying admixture (VMA) to prevent segregation in the concrete. In this research project, instead of VMA, high volumes of fly ash were used along with superplasticizer to develop SCC. The minimum use of superplasticizer and optimum use of fly ash were desired to achieve required properties of SCC. The fly ash is expected to be useful not only in generating the flow but as segregation resistance as well. The aim of the present research project was to develop SCC for sustainable construction by optimizing the use of high volumes of fly ash with some proposed statistical models. The rheological study for paste and mortar was carried out first, and Bingham model parameters such as plastic viscosity and yield stress were correlated with the marsh cone flow of paste and fresh concrete properties such as slump flow and filling capacity. The limits for plastic viscosity, yield stress, and specific marsh cone flow of paste and mortar were identified for concrete mixes to be qualified for SCC. Four independent variables such as total binder content (limit 350 to 450 kg/m3), percentage of fly ash replacing cement (limit 30 to 60 %), % of superplasticizer (limit 0.1 to 0.6 %), and W/B (limit 0.33 to 0.45) were considered for design of experiment and for development of statistical models for SCC. Statistically balanced twenry-one concrete mixes were chosen and fresh concrete tests such as slump and slump flow, V -funnel flow, filling capacity, L-box, bleeding, air content, segregation, and initial and final setting time tests were performed. Seven harden concrete tests (for mechanical characteristics and durability) such as compressive strength (1, 7, 28-day), freezing and thawing cycles resistance, surface scaling resistance, rapid chloride permeability, modulus of elasticity, flexural strength, and drying shrinkage were performed to evaluate the performance of SCC. Five statistical models for important properties of SCC such as slump flow, I-day strength, 28-day strength, rapid chloride permeability, and material cost were developed. The limits of rheological parameters of pastes and mortars can be useful to predict the flow behavior of SCC and the proposed models can be useful to design and optimize SCC mixes incorporating high volumes of fly ash.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".