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Record W4402059331 · doi:10.1061/9780784485569.053

Pipe Diameter Is a Dominant Factor in Pipe Failure Risk

2024· article· en· W4402059331 on OpenAlexaff
Kevin Laven, Shaoqing Ge, Marco Dignum, Michael Zantingh, Nimarta Gill, K. Ponnambalam, Harry Krinas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsAmerican Water (Canada)University of Waterloo
Fundersnot available
KeywordsFactor (programming language)Computer scienceForensic engineeringReliability engineeringEngineeringProgramming language

Abstract

fetched live from OpenAlex

Pipe diameter is a key factor in water main design. Decisions around what diameter to use are generally driven by hydraulic considerations, such as offering sufficient flows and residual pressures during fire suppression usage. Considerations of the relative failure risks of different diameters are rarely taken into account; however, this novel research finding suggests that diameter is a much more significant factor than was previously understood. Previous studies have noted that small diameter mains are responsible for more leaks than transmission mains, and that customer services suffer more leaks than either of the two. These observations have come through the professional observations of experienced engineers and from small scale studies. Other observations have noted potential errors in the theory underpinning the design standards for small diameter pipe. None of these have previously translated to a systematic examination of the relationship between diameter and failure risk. This paper presents a novel finding of an inverse-linear relationship between pipe diameter and failure risk per unit length of pipe. This finding is supported by the largest database of pipe failure records ever compiled, including over 10 million pipe-years of records from nine utilities across North America, Europe, and Asia. These records show a clear and strong negative correlation between diameter and failure risk across all pipe materials. When the inverse of diameter is considered, the relationship with failure risk appears to be linear for many materials. This relationship has two major implications. First and foremost, the fact that small diameter pipes fail as much as 100x as frequently as large diameter mains should be incorporated into risk considerations during pipe design and asset management decisions. Second, it offers a novel and useful normalization: failures per million pipe diameters of length per year. This normalization provides a more robust and consistent measure of failure risk than the industry norms of failures per 1,000 pipes per year or failures per 100 km (or 100 mi) per year. That in turn allows the actual failure risk posed by other factors (in particular pipe material, which often correlates with diameter) to be more clearly demonstrated, exposing some misconceptions about the relative risk of different pipe materials.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.644
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.190
Teacher spread0.184 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2024
Admission routes1
Has abstractyes

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