Synthesis and Characterization of Cement/Carbon-Nanotube Composite for Structural Health Monitoring Applications
Bibliographic record
Abstract
The civil structures during their useful life are subjected to different loads (environmental and mechanical), that cause progressively deterioration and that consequently requires maintenance efforts to keep them safe and operative.In this work, a novel alternative is proposed based in the development of a smart material with multifunctional characteristics that allow the Structural Health Monitoring (SHM)without requiring external sensors.This was possible by developing cement matrix compounds with the addition of carbon nanotubes.The manufacturing of this material began with an experimental design (DOE) that allowed to determine the mixturestype with which the sensor was manufactured, additionally repeatability and reproducibility was guaranteed.To measure the piezoresistive behavior of the samples, we used an INSTRON 5582 universal testing machine, a data acquisition equipment to obtain the variation in voltage and Micron Optics SM130 optical sensing interrogator for measuring deformations.Once the rheological and mechanical behavior of the material were characterized, mechanical tests of the material were carried out without exceeding the maximum deformations of the material in order to acquire data on the resistive behavior of the structure and find the gauge factor.The results of this development offer a part of the solution to the growing needs in the field of civil engineering against the early warning of damages caused by both natural phenomena and human causes (use of inappropriate materials, wrong calculations, overloads).
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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) | 0.001 | 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 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".