Integrated sensing technologies for detection and location of leaks in water distribution networks
Bibliographic record
Abstract
Abstract Water distribution networks (WDNs) are infrastructure systems that have high socioeconomic values, for which efficient operation and management are required to ensure minimal amounts of waste which can be represented in the form of leaks. Leak detection is considered as one of the challenges faced by municipalities operating WDNs because it either involves shutting down the system or requires using expensive equipment and technologies. In this paper, a novel noninvasive and nondestructive methodology for detecting leaks in water pipes was tested. Ground penetrating radar was used for accurate determination of pipe location, followed by infrared (IR) thermographic imaging for determining the leak location using four different operating conditions. Results were statistically analyzed using analysis of variance and pairwise comparison methods. Several factors were found to affect the accuracy of the proposed methodology in predicting the leak location, namely, the characteristics of the studied surface (i.e. emissivity), the characteristics of the surrounding environment (i.e. ambient temperature and relative humidity), and the operating conditions of the IR camera (i.e. speed and height of the camera). The results obtained in this study have also shown that under high ambient temperatures and high relative humidity conditions, a higher speed of the IR camera would reduce the impact of noise on the collected thermal contrast and therefore, would give better leak location prediction results. The tested methodology proved the flexibility of the approach and the ability of accurately predicting the leak locations under different conditions.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".