Field Validation of Concrete Transport Property Measurement Methods
Bibliographic record
Abstract
Reinforcing steel corrosion, caused by chloride ingress into concrete, is the leading cause of reinforced concrete deterioration. One of the main findings in the literature for reducing chloride ingress is the improvement of the durability characteristics of concrete by the addition of supplementary cementitious materials (SCMs) and/or chemical agents to concrete mixtures. In this study, standard ASTM tests-such as rapid chloride permeability (RCPT), bulk diffusion and sorptivity tests-were used to measure concrete properties such as porosity, sorptivity, salt diffusion, and permeability. Eight different mixtures, prepared with different SCMs and corrosion inhibitors, were tested. Apparent and effective chloride diffusion coefficients were calculated using bound chloride isotherms and time-dependent decrease in diffusion. Diffusion coefficients decreased with time, especially with the addition of SCMs and corrosion inhibitors. The apparent diffusion coefficient calculated using the error function was slightly lower than the effective diffusion coefficient; however, there was a linear trend between the two. The formation factor was found to correlate with the effective diffusion coefficient. The results of the laboratory tests were compared and benchmarked to their counterparts in the marine exposure site in the Arabian Gulf in order to identify laboratory key tests to predict concrete durability. The overall performance of concrete containing SCMs, especially fly ash, were the best among the other mixtures in the laboratory and the field.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".