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Probability of Failure Analysis due to Internal Corrosion in Cast-Iron Pipes

2010· article· en· W1966105912 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Infrastructure Systems · 2010
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsCorrosionMonte Carlo methodCast ironMaterials scienceChlorineReliability (semiconductor)MetallurgyMathematicsPhysicsThermodynamics

Abstract

fetched live from OpenAlex

In cast-iron pipes, chlorine consumption due to internal corrosion is high compared with consumption caused by other water-quality factors in such systems and may be considered as an approximate indicator of the rate of internal corrosion. Herein, a relationship is developed that determines the rate of internal corrosion as a function of the chlorine concentration, the chlorine decay constant, and the velocity of the water in a pipe segment with a given length and diameter. These factors may be considered uncertain at any given time and the probability of mechanical failure due to the thinness of the pipe wall caused by internal corrosion throughout the pipe life is estimated using Monte Carlo simulation, the first-order reliability method, and the second-order reliability method to account for these uncertainties. The results indicate that the likelihood of failure is nearly 50% by the 80th year for a cast-iron pipe length of 6 m and a diameter of 203 mm, and that the affect of internal corrosion on the pipe wall thickness and thus mechanical failure is lower than, but on the same order of magnitude as, the effect of external corrosion.

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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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