Analysis and Verification of PCCP Stiffness Testing Using Non-Invasive Acoustics
Bibliographic record
Abstract
Life cycle management of pre-stressed concrete cylinder pipe (PCCP) presents unique challenges for water utilities, given concerns for catastrophic failures. New Jersey American Water experienced a significant rupture on a 48” diameter PCCP pipeline in 2012. The utility’s team began evaluating available PCCP test methodologies in 2013 to accelerate their knowledge of condition of the 172+ miles of PCCP in their network. Acoustic wave propagation (AWP) testing was investigated given its ability to quickly test large amounts of pipeline, minimize water supply interruptions, and reduce customer disturbances. AWP survey-level testing can be used to identify concrete pipeline segments with reduced structural stiffness. Reduced pipe wall stiffness in concrete mains may be an indicator of broken prestressing wires, lower prestress, deteriorated mortar coating, cracked concrete core, and other issues. The presentation describes AWP stiffness testing in PCCP mains, and the structural and failure risk analyses (and external pipe inspection results) used to evaluate the predicted pipe stiffness. Test results are evaluated based on variability of the effective (measured) pipe stiffness within a given pipe class, and comparison of the measured versus nominal (calculated) pipe stiffness. The presentation will also include an overview of New Jersey American Water’s tiered approach to pipe condition assessment, and a case study focused on a critical 60” PCCP main.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".