Structural damage detection under multiple stiffness and mass changes using time series models and adaptive zero‐phase component analysis
Bibliographic record
Abstract
Nowadays, there is a considerable effort to develop technologies for smart cities. Smart buildings are a critical component of such smart cities, and their automated structural health monitoring is essential. This paper presents a new, efficient, and robust methodology for automated structural damage detection of shear-type buildings. The proposed method uses output-only acceleration response to separately detect changes in stiffness and mass using adaptive zero-phase component analysis (AZCA) in conjunction with time series analysis, that is, autoregressive moving average models with exogenous inputs (ARMAX). In our efforts to tackle the effects of operational factors on structural damage detection processes, herein, mass changes are differentiated from structural damage. Assuming the mass at one DOF at any location is constant (a priori knowledge about the location is not needed), changes in the ARMAX model coefficients are then employed to build stiffness change features (SCFs) and mass change features (MCFs) from which changes in mass and stiffness can be detected separately. A four-story shear structure was constructed in the laboratory to experimentally validate the proposed methodology. The experiment results demonstrate that the approach is successful in eliminating mass effect to determine the existence, location, and severity of the structural damage accurately.
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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) | 0.001 | 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".