Systematic Sensor Data-Driven Analysis Pipeline for Anomaly Monitoring of Bridges and Rails
Bibliographic record
Abstract
Regular monitoring of bridge and rail structural abnormalities is inseparable from the application of industrial sensors, especially the data analysis of new sensors, such as displacement, temperature, strain and other measurement sensors. The data-driven analysis model based on machine learning provides the possibility of long-distance, long-term, and abnormal state monitoring for early warning and effective management of important structural components. This paper presents a systematic data-driven analysis pipeline for anomaly monitoring. Firstly, data correlation analysis is performed to ensure that prediction models can benefit from strong correlations. Next, a density distribution analysis of statistical signals is established, enabling the reconstruction of global data signals within a certain bandwidth to compensate for data missingness and errors. Subsequently, a seasonal Auto-regressive Integrated Moving Average (ARIMA) model is developed for data prediction, providing reference values for abnormal behavior monitoring. Particularly, we propose the utilization of kurtosis to introduce long-term memory models. By performing cumulative prediction and comparing it with the measured values from continuous time series, an effective method for detecting anomalies can be achieved. Overall, our systematic approach effectively promotes the intelligent development of bridge health and safety monitoring.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".