Photoelectric sensors for wireless monitoring of bridge scour – laboratory investigation and field validation
Bibliographic record
Abstract
Scour, or the erosion of bed material is a major cause of bridge failure across the world. Monitoring scour levels at bridge foundations reduces the risk of failure through timely condition-based maintenance. This paper evaluates the use of photoelectric sensors for scour detection through laboratory studies and subsequent field investigation. Two types of photoelectric sensors, namely diffusive-reflective and through-beam, were independently investigated. The sensors were installed at six distinct depths on a simulated bridge pier in a laboratory flume. Scour resulting from hydrodynamic action triggered the sensors at different levels, enabling scour depth detection. An inverse response from the sensors detected scour refill. Following successful laboratory tests, a photoelectric scour-sensing prototype was installed in a small creek in August 2019 which continued to monitor scour until April 2022. The prototype response confirmed laboratory results and continues to perform well under various field conditions such as rain, debris, and snow. The very low-cost system required minimal power and bandwidth, and the sensing component was robust to flow parameters. Long-term field studies are required to evaluate their susceptibility to biofouling and develop biofouling countermeasures.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".