Self-sensing nano-engineered ultra-high performance concrete (UHPC) under tension
Bibliographic record
Abstract
With the addition of electrically conductive steel or carbon fibers, Ultra-High-Performance Concrete (UHPC) possesses an intrinsic self-sensing capability. This opens up the possibility of combining the resilience and sustainability of UHPC with the development of self-sensing solutions for structural applications. In this study, the self-sensing behaviour of a proprietary nano-engineered UHPC material subjected to tension was investigated. To assess the self-sensing performance of the material, bulk resistivity measurements were used on direct tension and pure flexure tests, while a novel wireless approach that operates on was used on out-of-plane bending tests. The wireless approach used alternate current (AC) measurements while the bulk resistivity methods were performed through direct current (DC) and the four-probe method. In both methods, the fractional change in resistance was correlated to the state of deformation. The disposition of the actual strain field was evaluated using Digital Image Correlation (DIC). It was found that in the case of direct tension and pure flexure, the fractional change of resistance was initially decreased up to the onset of strain localization, while it gradually increased in the post-peak range, where the separation of the localized crack gradually increased. In the case of the wireless approach using AC, the onset of cracking was successfully predicted with an abrupt increase in resistivity. The wireless strain-sensing approach also captured the Poisson’s effect due to the loading.
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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 | 0.000 | 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 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".