Rapid and Automated Damage Detection in Buildings Through ARMAX Analysis of Wind Induced Vibrations
Bibliographic record
Abstract
After a seismic event, it is imperative that critical structural members that are damaged within a building are identified and analyzed as soon as possible to ensure proper remedial measures can be taken. Failure to detect damage or correctly analyze the severity of damage within the building could have catastrophic consequences. When a reinforced concrete building is subjected to a damaging event, the current standard method for identifying and analyzing structural damage involves extensive surface-level visual inspections which often result in inconclusive and inconsistent damage analysis. Structural Health Monitoring (SHM) is a rapidly developing field which is vastly improving the way damage is assessed within buildings and other major infrastructure. In this paper, an automated SHM Damage Detection Model (DDM) specifically tailored for buildings is developed that uses time series analysis along with sensor clustering techniques to detect damage in a building from its vibration response due to ambient wind loading. The specific time series analysis methodology used throughout this paper is an Auto-Regressive Moving Average model with eXogenous inputs (ARMAX). To validate the ARMAX DDM, a detailed wind simulation model that applies forces based on actual wind behaviour is created along with a numerical damage model applicable to reinforced concrete buildings. To evaluate the effectiveness of the proposed DDM in locating and quantifying damage at storey level precision, two buildings are modelled in SAP2000. The results from the numerical modelling proved the effectiveness of the ARMAX DDM at accurately locating and quantifying the degree damage from wind induced floor vibrations at a storey level precision. The limitations of the DDM in its current state and recommendations for future work are discussed to conclude the paper.
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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) | 0.000 | 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".