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Record W3000186695 · doi:10.1139/cjce-2019-0229

Material properties for fracture mechanics based strength assessment of cast iron water mains

2020· article· en· W3000186695 on OpenAlexafffundvenueabout
Suborno Debnath, Ashutosh Sutra Dhar, Premkumar Thodi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsCentre For Cold Ocean Resources EngineeringMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFracture mechanicsCast ironFracture toughnessMaterials scienceFracture (geology)Structural engineeringUltimate tensile strengthStrength of materialsGeotechnical engineeringMechanicsEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Municipal water distribution systems in Canada and other countries include a large number of cast iron pipes that were installed almost 50 years ago. For structural integrity assessment of these pipes, the fracture mechanics approach is found to be more effective than the conventional continuum mechanics-based approach. This paper presents the mechanical properties for fracture mechanics-based strength assessment of water mains determined through testing of cast iron pipes exhumed from two cities in Canada. Microstructure analysis is conducted to understand the materials’ inherent properties. Uniaxial tensile tests are conducted to determine the stress–strain relations. The influence of the rate of loading on stress–strain behaviour and loading-unloading responses are investigated. A simplified single-edge notch beam test is used to obtain the fracture toughness. Probabilistic distributions of the parameters are provided to account for the uncertainty and variabilities observed in the test results.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.704

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 head0.009
GPT teacher head0.179
Teacher spread0.170 · 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

Citations4
Published2020
Admission routes4
Has abstractyes

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