Displacement prediction for long-span bridges via limited remote sensing images: An adaptive ensemble regression method
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".