The use of random decrement technique for long term health monitoring of concrete structures
Bibliographic record
Abstract
This investigation deals with the development of an advanced strategy for Structural Health Monitoring (SHM) of concrete beams and girders for important structures such as bridges, offshore platforms and nuclear power stations using smart monitoring systems, including an effective diagnostic approach for damage detection with a reliability-based performance ranking. The proposed strategy can be classified into four main sections: identification of the existence of damage, determination the localization of the damage, estimation the level of damage, and assessing the seriousness of the damage regarding the structure’s service life. Random Decrement (RD) is an accurate dynamic analysis diagnostic tool that has been used effectively for SHM; the technique has been used within various fields in mechanical, aerospace, and, recently, civil engineering. The damage detection approach is based on the RD technique. A theoretical, numerical, and experimental investigation has been conducted on concrete beams using the RD technique for damage detection in terms of changes in the dynamic properties and used at successive multiple points under certain leading point conditions to determine the location of damage through the development of the Multi-Channel Random Decrement (MCRD).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".