Nonlinearity as a damage index for structural health monitoring using random decrement technique
Bibliographic record
Abstract
Structural health monitoring (SHM) implements prominent methods for detecting the evolution of damage. This study detects damage by implementing the Random decrement (RD) technique based on nonlinear damping analysis for 12 reinforced concrete (RC) columns under compression up to failure. In addition, employing a second-generation fibre-optic accelerometer for measuring vibration responses is evaluated. The specimens’ design variables are three different concrete types with two different configurations solid and hollow. Six specimens are the same as the remaining, except they underwent submerged curing. A vibration-based damage identification technique (VBDIT) was performed on all the columns to index the thresholds-limits of damage induced by progressive compressive loading at 0.1 % strain, yield, and pre-ultimate states. RD signatures effectively generate the damage indexes by obtaining changes in dynamic parameters through adequate linear and nonlinear system assumptions. Besides, the columns’ responses under compressive loading were expressed regarding the load-deformation relationships, failure modes, ductility, and toughness. A purely viscous dissipative mechanism is observed in all the columns with the same failure condition at intact, 0.1 % strain, and yield states. At the pre-ultimate state, nonlinearity occurred in the damping ratio of all the columns. The combined viscous with nonlinear damping parameters coulomb-cube root are employed to derive the nonlinear damage indexes by applying the adopted energy approach and proposed zoning approach models. The damage indexes outcome from viscous damping and frequency show inconsistencies. Conversely, the nonlinearity damage index is highly consistent. Among all nonlinearity models, the zoning approach is recommended as it incorporates the static-dynamic friction spectrum. • A novel damage detection method is presented on RC columns under compression. • Vibration-based damage detection technique of Random decrement is implemented. • To detect damage, a cutting-edge second-generation FBG accelerometer is employed. • Thresholds and limits of damage are indexed under linear and nonlinear assumptions. • Nonlinearity is identified as a key damage indicator via proposed proper models.
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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 | 0.001 |
| 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".