Optimizing the Fabrication of Cementitious Sensors for Structural Health Monitoring
Bibliographic record
Abstract
Cementitious sensors function based on the principle of piezoresistivity, which is defined as the dependence of electrical resistivity on applied strain. These sensors incorporate conductive materials within a cementitious matrix. Fiber dispersion quality, mix design, and fabrication methods significantly impact the sensitivity, repeatability, and stability of the sensors. In this study, cementitious sensors were fabricated using coal-tar pitch-based carbon fibers as the conductive phase and the Taguchi method of optimization was utilized to find the most effective mix design and fabrication procedures. Four-probe electrical resistivity measurement under compressive mechanical loading determined the efficiency of each fabrication setting. The fiber dispersion quality was evaluated via several image processing techniques. Optimization results indicated that sensor samples containing 15% volume fraction of carbon fiber, general use Portland cement and silica fume, and mixed with centrifugal mixer produce the best results with better repeatability.
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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 | 0.001 | 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) | 0.001 | 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".