Fuzzy logic approach for estimating durability of concrete
Bibliographic record
Abstract
In this work, it is shown how fuzzy inference systems can be constructed to produce a global durability evaluation factor for concrete based on various performance criteria from accelerated tests. This can facilitate the decision-making process during the design stage by identifying optimum concrete mixtures proposed for specified field exposure. A fuzzy inference system was built for the specific case of various self-consolidating concrete mixtures subjected to ammonium sulfate attack. The performance of this model was compared with that of other models that enable decision making: the remaining service life model and compromise programming. Results of the fuzzy inference system had a better correlation with compromise programming (R 2 = 0·7) than that with the remaining service life model (R 2 = 0·5), and better represented the actual degradation observed in test specimens. It is shown that the proposed fuzzy inference model is rational, clear, reliable, versatile and flexible since it can be easily updated with new data or modified to accommodate future findings.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".