Resistivity, Penetrability and Porosity of Concrete: A Tripartite Relationship
Bibliographic record
Abstract
Abstract Demands for using electrical resistivity techniques (surface and bulk resistivity) as an alternative to the rapid chloride penetrability test (RCPT) have been growing, for example by a number of transportation agencies in North America, to give an indication of the relative penetrability of concrete. While resistivity measurements may reflect the quality of pore structure in the cementitious matrix, their accuracy might be affected by a multitude of parameters including the concentration of ionic species in the pore solution, particularly when supplementary cementitious materials (SCMs) are incorporated in the binder. Hence, a systematic investigation on the resistivity of concrete and its corresponding physical penetrability is warranted. The main objective of this study was to establish a tripartite relationship (nomogram) to correlate surface resistivity with penetrability (migration coefficient) and porosity of concrete using a wide range of concrete mixtures, taking into account the effect of key mixture design parameters (water-to-binder ratio, air-entrainment, SCMs, and type of cement). Relationships between surface and bulk resistivity as well as migration coefficient and porosity of concrete were also established. In addition, a penetrability classification of concrete based on the corresponding ranges of surface resistivity, migration coefficient, and porosity has been proposed. The efficacy/practicality of the proposed classification was evaluated by comparing the predicted migration coefficient and porosity, based on surface resistivity of concrete, to the measured ones for field cores extracted from concrete pavement in Winnipeg, Canada. For newly constructed and aging pavement, the nomogram and penetrability classification provided reasonable assessment for the condition of field concrete.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".