Nondestructive testing of externally reinforced structures for seismic retrofitting using flax fiber reinforced polymer (FFRP) composites
Bibliographic record
Abstract
Natural fibers constitute an interesting alternative to synthetic fibers, e.g. glass and carbon, for the production of composites due to their environmental and economic advantages. The strength of natural fiber composites is on average lower compared to their synthetic counterparts. Nevertheless, natural fibers such as flax, among other bast fibers (jute, kenaf, ramie and hemp), are serious candidates for seismic retrofitting applications given that their mechanical properties are more suitable for dynamic loads. Strengthening of structures is performed by impregnating flax fiber reinforced polymers (FFRP) fabrics with epoxy resin and applying them to the component of interest, increasing in this way the load and deformation capacities of the building, while preserving its stiffness and dynamic properties. The reinforced areas are however prompt to debonding if the fabrics are not mounted properly. Nondestructive testing is therefore required to verify that the fabric is uniformly installed and that there are no air gaps or foreign materials that could instigate debonding. In this work, the use of active infrared thermography was investigated for the assessment of (1) a laboratory specimen reinforced with FFRP and containing several artificial defects; and (2) an actual FFRP retrofitted masonry wall in the Faculty of Engineering of the University of L’Aquila (Italy) that was seriously affected by the 2009 earthquake. Thermographic data was processed by advanced signal processing techniques, and post-processed by computing the watershed lines to locate suspected areas. Results coming from the academic specimen were compared to digital speckle photography and holographic interferometry images.
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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".