Leak detection in an operational underground water distribution network using active acoustics
Bibliographic record
Abstract
Leaks present in the water distribution networks (WDNs) lead to an enormous loss of a valuable resource, not only affecting the economic efficiency of water utilities but also posing significant potential safety hazards, and an increased burden on infrastructure maintenance. In the past, several studies have focused on passive acoustic-based leak detection by primarily sensing and analyzing the acoustic signals emitted from the leak source. In this study, we installed a low-frequency transducer in the water column of an operational WDN near Los Angeles, California, and excited the system using steady-state sinusoidal and sine burst signals. The acoustic pressure signals inside the pipe network were measured at multiple locations using state-of-the-art hydrophone-enabled devices retrofitted to fire hydrants. The experiments were conducted in the presence of a simulated leak in the network. The leak acts as an impedance discontinuity for the acoustic wave propagation, and therefore, the excited signals undergo partial reflection at the leak location. Based on signal processing techniques, we attempt to detect and localize the leaks in the WDN. The goal of using the active acoustics is to detect small leaks and increase the range of sensing.
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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 | 0.000 | 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 | 0.001 |
| 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 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".