Linking Concrete Rheology to Strength: Sustainability Model Approach
Bibliographic record
Abstract
A requirement for achieving sustainable concrete structures is to develop a quantitative method for designing concrete mixtures that yields the target rheological properties and compressive strength.Toward this objective, this paper proposes a mathematical model approach to improve the sustainability of the concrete industry.A postulation that packing density, a function of the concrete mixture, provides the link between concrete mixture, rheological properties, and compressive strength was investigated.Rheological models for yield stress and plastic viscosity, and a compressive strength model were adopted with packing density as a central variable.The rheological models employ a cell description that is representative of fresh concrete.The compressive strength model is based on excess paste theory to account for the concrete mixture proportions, gradation of aggregate particles, and porosity.An experimental program was developed to calibrate and test these models.Results revealed that packing density provides a consistent and reliable link, and that the concrete mixture composition can be designed to achieve the target rheological properties and hardened properties and ensure quality control.Consequently, a new mixture proportioning methodology was developed and proposed as an improvement to the ACI 211.1 mixture design method.Furthermore, a case study was conducted to test for the applicability and adequacy of this proposed method.This research outcome, which provides a quantitative approach to design concrete mixtures to meet specific strength requirements and rheology, can also be used to ensure quality control before concrete is cast.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".