The reseach on PCCP risk management based on wireless sensor network
Bibliographic record
Abstract
During the recent years, numerous utilities have experienced catastrophic rupture of critical Prestressing Concrete Cylinder Pipe (PCCP) lines throughout the world. Because PCCP is usually used throughout many water and wastewater utilities as critical mains with high flow rates, sudden failures can have significant negative consequences. Much attention has been focused on reliably assessing the condition of PCCP mains. Some techniques, such as visual inspections, electromagnetic inspections, acoustic monitoring and fiber-optic monitoring, are used to assess the condition of a pipeline now. Each of these techniques has capabilities and limitations that are important to understand when assessing the condition of a main. This can lead to the adoption of inadequate or over-conservative mitigation strategies. In this paper the combination of continuous acoustic monitoring and comprehensive dynamic risk management modeling is proposed. It provides an assessment of remaining time to failure for each pipe segment. This strategy can provide the opportunity to identify problematic pipe sections and repair the pipe prior to failure, and also can assess the presence and extent of deterioration in these large-diameter water and wastewater pipelines.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".