A kernelized deep regression method to simultaneously predict and normalize displacement responses of long-span bridges via limited synthetic aperture radar images
Bibliographic record
Abstract
Synthetic aperture radar (SAR) images retrieved by spaceborne remote sensing have recently gained significant attention as an affordable and effective solution to provide structural responses in terms of displacements from field measurements. Notwithstanding, this process may lead to partial/scattered information due to the limitations of SAR images. Furthermore, the effects of unmeasured environmental and/or operational conditions on structural responses and sensitivity of SAR-extracted displacements of full-scale structures like long-span bridges to these conditions still stand as major challenges. In this work, an innovative machine learning-aided methodology is put forward for handling these issues. The proposed methodology simultaneously predicts and normalizes displacement data within a two-stage kernelized deep regression (KDR) framework. The first stage involves kernelized regressor modeling and selection, exploiting Gaussian process regression and support vector regression. The second stage is based on deep regressor modeling via a long-short-term-memory neural network. The proposed methodology is shown to display high accuracy in prediction limited displacement data independent of unmeasured environmental/operational data. To concretely assess the performance of the proposed methodology, displacement responses from two long-span bridges and seasonal temperature records are considered. Results show that the approach is superior to available state-of-the-art techniques.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".