Photoelectric sensors for wireless monitoring of bridge scour – laboratory investigation and field validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it