Finite element model updating for the dynamic response of continuous deck railway viaducts leading to efficient digital twins
Bibliographic record
Abstract
Abstract Bridges and viaducts are key infrastructure elements within high speed railway lines, for which dynamic actions due to traffic are one of the main actions for design of new structures or evaluation of existing ones. This paper describes techniques based on experimental measurements and numerical finite element models leading to efficient digital twins, to be used for predictive maintenance, within the framework of an innovative prototype for a continuous predictive maintenance system installed in the La Marota 381 m long viaduct, in the high-speed line between Córdoba and Málaga in Spain. This system includes both model-based methods as well as data-based methods for maintenance decisions. Here we focus on the work for model-based acceleration performance, comparing in real time measurements from accelerometer sensors and predictions from a digital twin. The digital twin is an autonomous structural modal analysis software derived from a finite element model previously calibrated with Operational Modal Analysis experimental data. Results obtained from 3 representative high-speed trains are shown and compared in terms of root-mean-square (RMS) and moving window RMS of accelerations between the measured and numerical predictions. These allow to establish criteria and thresholds for predictive maintenance alerts.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".