Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring (SHM) of these infrastructures is essential to reduce life-cycle costs, and determine their remaining life using advanced sensing techniques and data fusion methods. However, the data obtained from the SHM systems describing the health condition of the infrastructure systems may contain anomalies (i.e., distortion, drift, bias, outlier, noise etc.). An automated framework is required to accurately classify these anomalies and evaluate the current condition of these systems in a timely and cost-effective manner. In this paper, a recursive and interpretable decision tree framework is proposed to perform multiclass classification of acceleration data collected from a real-life bridge. The decision nodes of the decision tree are random forest classifiers that are invoked recursively after synthetically augmenting the training data before successive iterations until suitable classification performance is obtained. This machine-learning-based classification model evolved from a simplistic decision tree where statistical features are used to perform classification. The feature vectors defined for training the random forest classifiers are calculated using similar statistical features that are easy to interpret, enhancing the interpretability of the classifier models. The proposed framework could classify non-anomalous (i.e., normal) time-series of the test dataset with 98% accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it