Displacement prediction for long-span bridges via limited remote sensing images: An adaptive ensemble regression method
Why this work is in the frame
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Bibliographic record
Abstract
• Proposing a novel predictive method based on an adaptive ensemble regression algorithm. • Leveraging advanced ML algorithms such as kernel learning, ensemble learning, incremental learning, and hybrid learning. • Simultaneous predicting and normalizing using limited data. • Validating real-world data of long-span bridge. Spaceborne remote sensing via synthetic aperture radar (SAR) images offers promising solutions to long-term structural health monitoring by providing local displacement time histories. However, this methodology faces challenges such as limited image accessibility, data sparsity, and real-time monitoring feasibility. Although regression-based prediction is a practical approach to deal with these limitations, the availability of limited SAR-extracted displacement data and the impacts of unmeasured environmental/operational factors lead to extra challenges that can skew prediction outputs. To overcome these issues, this article proposes a novel adaptive ensemble regression method that not only predicts displacement time series from limited SAR images but also simultaneously removes environmental/operational variability in predicted displacements. This method features two levels of kernelized and adaptive regression modeling within a sequential ensemble learning framework using Gaussian process regression as the primary regressor. Results from two real-world bridge structures substantiate the effectiveness of the proposed method in simultaneous prediction and normalization.
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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 | 0.001 | 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