Embedded NMR Sensor to Monitor Compressive Strength Development and Pore Size Distribution in Hydrating Concrete
Bibliographic record
Abstract
In cement-based materials porosity plays an important role in determining their mechanical and transport properties. This paper describes an improved low–cost embeddable miniature NMR sensor capable of non-destructively measuring evaporable water loss and porosity refinement in low and high water-to-cement ratio cement-based materials. The sensor consists of two NdFeB magnets having their North and South poles facing each other, separated by 7 mm to allow space for a Faraday cage containing a Teflon tube and an ellipsoidal RF coil. To account for magnetic field changes due to temperature variations, and/or the presence of steel rebars, or frequency variation due to sample impedance, an external tuning circuit was employed. The sensor performance was evaluated by analyzing the transverse magnetization decay obtained with a CPMG measurement from different materials, such as a polymer phantom, fresh white and grey cement pastes with different w/c ratios and concrete with low (0.30) and high (0.6) w/c ratios. The results indicated that the sensor is capable of detecting changes in water content in fresh cement pastes and porosity refinement caused by cement hydration in hardened materials, even if they are prepared with a low w/c ratio (w/c = 0.30). The short lifetime component of the transverse relaxation rate is directly proportional to the compressive strength of concrete determined by destructive testing. The r2 (0.97) from the linear relationship observed is similar to that obtained using T2 data from a commercial Oxford Instruments 12.9 MHz spectrometer.
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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 | 0.000 | 0.000 |
| 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".