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Record W4413135659 · doi:10.1061/9780784486382.062

Condition Assessment and Rehabilitation Options for Peters Pipeline: A Case Study of Large-Diameter Raw Water Main Rehabilitation Study

2025· article· en· W4413135659 on OpenAlexaff
Ali Alavi, Darrel Evensen, Matthew Petersen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsRehabilitationPipeline (software)Pipeline transportForensic engineeringEngineeringComputer scienceEnvironmental sciencePhysical therapyMedicineEnvironmental engineeringMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents a condition assessment and rehabilitation analysis of the Peters Pipeline, a 3.2-mi long, 78″ to 63″ cast-in-place concrete pipe (CIPCP) raw water main operated by Stockton East Water District (SEWD). The study employed closed-circuit television (CCTV) inspection for the bare concrete section and visual inspection with sounding for the HDPE-lined section. Defect analysis utilized NASSCO’s Pipeline Assessment Certification Program (PACP) to grade defects, calculate pipe ratings, and determine collapse stages for each pipe segment. Significant defects were identified, including circumferential cracks and fractures in the non-lined section, and liner separation, bulging, and infiltration in the lined portion. The study evaluated various rehabilitation options, including cured-in-place pipe (CIPP), sliplining, point repairs, and full replacement, considering benefits, disadvantages, constructability, and cost. Three rehabilitation scenarios were proposed: full-length renewal/replacement, targeted renewal with point repairs, and spot repairs to defer future costs. The impact of rehabilitation on conveyance capacity was assessed using an updated hydraulic model. Results showed that diameter reductions of up to 4 in. from CIPP or sliplining would have a negligible impact on desired flow, while an 8-in. reduction could decrease flow by 0.8 MGD. This study provides insights for water utilities facing similar challenges with aging large-diameter 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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.006
GPT teacher head0.289
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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