Bridge pier scour level quantification based on output-only Kalman filtering
Bibliographic record
Abstract
Soil scour near a bridge pier foundation is one of the leading causes of bridge failures. Traditional vibration-based scour monitoring methods are nearly incapable of quantifying scour levels using a single acceleration response without knowledge of excitation information. In this paper, a new output-only scour level prediction method is introduced via the integration of an unscented Kalman filter (UKF), random decrement (RD), and newly derived continuous Euler–Bernoulli beam addressing river water, traffic loads, and the linear and nonlinear behavior of sediments around the pier as external effects. We conducted extensive simulation studies and applied the method to an existing medium-span bridge with a steel girder and concrete deck in service in the province of Manitoba, Canada. These studies show that our proposed method can accurately estimate scour levels using only one accelerometer, which was validated with an independent bathymetric survey of the soil level at the pier foundation. Furthermore, three different linear and nonlinear soil profiles representing the soil behavior around the pier were also investigated as case studies in the scour level estimation process. The results confirm that a cubic function exhibits the best performance in quantifying the scour level around bridge piers.
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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 | 0.000 |
| 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".