Development and evaluation of an automation algorithm for a time-domain reflectometry bridge scour monitoring system
Bibliographic record
Abstract
Bridge scour is a major threat to the safety of bridges. There is a high risk of scour-induced damage due to the catastrophic nature of bridge foundation failure. The development of an innovative bridge scour monitoring system is a pressing task for the research community. Such a system needs to be fieldworthy, which is a characteristic assessed in terms of accuracy, ruggedness, and automation. Among these criteria, an automatic signal analysis algorithm is generally a prerequisite for deploying a long-term field monitoring program. This paper describes the development and validation of an algorithm for a scour monitoring system based on the principles of guided radar: time-domain reflectometry (TDR). This algorithm is based on the extension of the classic dielectric mixing model to layered systems. The performance of this algorithm is evaluated using experiments designed to simulate different field scour conditions. These include different types of sediments and the variation of river conditions (i.e., salinity of river water, air entrainment, and amount of suspended sediments). The experiment results indicate that the developed analyses algorithm is robust and accurate for scour-depth estimation under these investigated conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".