Self-sensing nano-engineered ultra-high performance concrete (UHPC) under tension
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it