Impact resonance method for damage detection in carbon-fibre-reinforced polymer–strengthened reinforced concrete beams subjected to fatigue and thermal cycling
Bibliographic record
Abstract
Much of North America’s civil infrastructure is rapidly aging and, in some cases, exceeding its design life and load. To combat this, the exploration of simple and effective methods for rehabilitation and structural health monitoring has been receiving much attention in industry and academia. This paper reports on the use of the impact resonance method (IRM) for evaluating the structural health of thermal-cycled reinforced concrete (RC) beams with and without externally strengthened carbon-fibre-reinforced polymer (CFRP) pultruded plates. In the experimental program, 1.2 m long specimens were subjected to 55 thermal cycles ranging from +23 to −18 °C. Fatigue loading consisting of up to two million cycles at high and low stress levels was performed. At pre-determined load cycle intervals, the loading was stopped and the IRM was performed on the specimens. Parameters including the appearance of the fast Fourier transform (FFT) spectrum of the specimens’ vibration, modal fundamental frequencies, and dynamic properties were used to assess damage in the specimen. Conclusions were made regarding the use of the IRM in monitoring the health of strengthened and unstrengthened RC beams subjected to thermal and fatigue cycles.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".