Improvements in forecasting and normalizing limited displacement responses of long-span steel bridges subjected to seasonal temperature variability
Bibliographic record
Abstract
Local displacements of large-scale bridges bring important information for structural health monitoring. Remote sensing through satellites and synthetic aperture radar (SAR) imagery displays significant opportunities to provide these displacements. However, challenges such as restricted access to images, inability to perform real-time monitoring akin to vibration-based SHM, and difficulties posed by large sizes and speckle noise of SAR images complicate their use in long-term monitoring programs. Environmental variability, especially seasonal temperature changes, can also influence displacements masking actual impacts of damage. To address these challenges, an innovative stacking ensemble regression method is proposed to simultaneously forecast and normalize small datasets of displacement responses retrieved from limited SAR images. This method comprises two levels of non-parametric base regressors and a parametric meta regressor trained by the predictions of the base learners along with the original response data. The base regressors are univariate, robust, and Bayesian linear regression models, while the meta regressor is developed from the ridge regression. The effectiveness and practicality of the proposed method are demonstrated through limited temperature and displacement samples of two large-scale steel bridges. Results show high prediction accuracy and successful normalization capabilities of the proposed method.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".