Analysis of the influence of material parameters on electrical conductivity of cement pastes and concretes
Bibliographic record
Abstract
This paper reports investigations on the influence of material parameters on electrical conductivity of cement pastes and concrete mixtures. The influence of cement type, water/cementitious materials ratio (w/cm), and the presence of fly ash as a cement replacement material on the conductivity of cement pastes is studied. The electrical conductivity–time relationships of cement pastes and concretes are expressed using a model that facilitates the extraction of initial and final conductivities, and a characteristic time parameter. These terms can be used to derive information about the microstructural changes occurring with time in cement pastes. A fractional factorial experiment scheme consisting of five factors—w/cm, fly ash content, aggregate–cementitious materials ratio (a/cm), aggregate size, and curing condition (saturated or sealed), with each factor at two levels (+1 or −1 corresponding to high and low levels)—is used for concrete mixtures. The experimental results are subjected to a range analysis to isolate the significant factors and factor interactions that influence the initial and final conductivities as well as the time parameter from the conductivity–time model for concrete mixtures. The a/cm exerts significant influence on both initial and final conductivities, whereas the amount of fly ash in the mixture, aggregate size, and curing condition influence the final conductivity of concretes. The w/cm and fly ash content were seen to influence the time parameter. Analysis of variance is conducted on the test results and the prominent two factor interactions that influence the conductivities and the time parameter are determined. The relationship between these responses and the parameters is expressed in terms of a least squares fit equation using the coded values for the variables.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".