A state-of-the-art review of active-thermometry techniques for bridge and pipeline scour monitoring, and exploratory passive thermometry studies
Bibliographic record
Abstract
This paper reviews the application of active thermometry techniques for bridge and pipeline scour monitoring, and explores the potential for passive thermometry through outdoor bucket-type static scour experiments. Active thermometry uses a device to supply heat and then monitors temperature loss. The heat generation is typically through resistive (joule) heating, and temperature is measured using digital temperature sensors, fiber optic temperature sensors, and thermistors. All laboratory studies in the literature were conducted in static conditions, in which the onset and progression of scour are detected by monitoring the changes in thermal properties using sensors placed along the bridge pier (or pipeline). The passive thermometry option explored in this study involved using DS18b20 digital temperature sensors to measure temperature variations in water and in three sediment types: clay, sand, and gravel. The results demonstrated larger diurnal variations in water than in the sediments. Sensors located in the sediment were distinguished from those in water by examining a combination of decrement ratios and phase shifts among the different temperature waveforms obtained for a finite number of diurnal cycles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".