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Record W3162952395 · doi:10.1139/cgj-2020-0591

Joint kinematics and sealing capacity assessment of ductile iron pipes under abrupt transverse ground movements

2021· article· en· W3162952395 on OpenAlexvenueno aff
Xiaogang Qin, Yu Wang, Cuiwei Fu

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersCity University of Hong Kong
KeywordsKinematicsJoint (building)Structural engineeringTransverse planeEngineeringDeflection (physics)Finite element methodGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

Joint kinematic behaviour, i.e., joint rotation and axial translation, can partially help pipelines to accommodate abrupt ground movements, and cause leaking if joint service limit is exceeded, even without any structural failure. Kinematic behaviour of bell-spigot jointed ductile iron (DI) pipes and its influence on joint sealing capacity under abrupt transverse ground movements are investigated in this study. Firstly, a beam-on-spring finite element analysis on joint kinematics of DI pipes is conducted, in which different fault–pipe crossing positions are implemented. Based on simulated results, a modified joint kinematic solution incorporating pipe deflection and joint shear force under different fault–pipe crossing positions is proposed. Then, a Monte Carlo simulation (MCS)-based reliability assessment procedure for joint sealing capacity is developed. Sensitivity analysis is subsequently conducted to investigate the effects of uncertainties associated with initial axial translation, soil properties, and crossing positions on the joint sealing capacity, and the effects of different deterministic solutions are compared. The proposed method allows engineers to effectively evaluate how the joint sealing capacity of DI pipes changes with consideration of uncertainties when abrupt transverse ground movements are encountered.

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: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.891

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 integrity0.0000.001
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 head0.016
GPT teacher head0.218
Teacher spread0.203 · 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

Citations30
Published2021
Admission routes1
Has abstractyes

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