Towards the Standardized Fabrication of CNT-Cement Based Composites for Structural Health Monitoring: An Application-Oriented Literature Survey
Bibliographic record
Abstract
Civil structures always experience degradation phenomena over their lifespan. The recent advances in nanotechnology and sensing allow to monitor the behaviour of a structure, assess its performance and identify damage at an early stage. The availability of innovative, high performance sensing tools gives the opportunity to carry out maintenance actions in a timely manner, and this definitely enhances the structural reliability and safety. Several Structural Health Monitoring (SHM) applications reported in the literature are traditionally performed at a global level, with a limited number of sensors distributed over a relatively large area of a structure. The main drawback with those systems is related to the possibility of detecting only major damage conditions. A recent progress in the field of civil SHM concerns the development of dense sensor networks and innovative structural neural systems. The latter reproduce the structure and the function of the human nervous system and provides interesting opportunities to overcome the typical limitations of SHM related to the poor spatial resolution of measurements. Miniaturization and embedment are key requirements for the successful implementation of structural neural systems. In this context carbon nanotubes (CNTs) play an attractive role in the development of embedded sensors and smart structural materials. In fact, they can provide to traditional cement based materials both structural capability and measurable response to applied stresses, strains, cracks and other flaws. As a result, cement based sensors can be developed and embedded, ensuring the maximum compatibility and minimum interference with the hosting structure. In this paper investigations about CNT/cement composites and their self-sensing capabilities are summarized and critically revised. The literature review has provided the informative basis for a rational analysis of published experimental results and theoretical developments.The result is an application oriented survey of the literature about CNT/cement composites for SHM. It provides useful design criteria for the standardized fabrication of CNT/cement composites optimized for SHM applications in civil engineering. Specific attention is paid to the opportunities provided by new RF plasma technologies for the functionalization of CNTs in view of sensor development and SHM applications.
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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 | 0.002 | 0.001 |
| 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".