Compressive strength model for concrete
Bibliographic record
Abstract
A predictive compressive strength model accounting for the type of cement, cement degree of hydration, aggregates type and gradation, mixtures proportion and air content was developed. This paper presents the formulation, implementation, calibration and validation of the proposed strength model for normal concrete. The theoretical formulation postulates that particles' interaction is governed by excess paste theory from which an average paste thickness model is developed to account for concrete mixture proportions and aggregate gradation. In addition, the model accounts for the cement compressive strength and aggregate to cement paste bond strength. An experimental programme, developed to evaluate the model, accounts for the following variables: water to cement ratio, water content, bulk volume and maximum size of coarse aggregate, and air content. The proposed model is found to accurately predict the strength of concrete mixtures at 3, 7, 28 and 191 days. The measured 3-day and 28-day strength range from 8·5 to 32·7 MPa and from 13·6 to 43·8 MPa, respectively. The corresponding standard error and correlation coefficient for the 3-day predictions are 2·1 MPa and 0·95, and 1·8 MPa and 0·96 for the 28-day predictions.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".