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Record W3083182703 · doi:10.1139/cjce-2020-0049

Review and recommendations for structural testing of buried gravity storm drain pipes and culverts

2020· article· en· W3083182703 on OpenAlexvenueno aff
Amin Darabnoush Tehrani, Zahra Kohankar Kouchesfehani, Mohammad Najafi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsCulvertGeotechnical engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Buried pipes are important components of the underground infrastructure. Structural failure of these pipes is costly, socially and environmentally disruptive. To prevent such incidents, a deep understanding of the soil–pipe interaction system behavior is needed. Currently, two common standard methods are available for the structural testing of pipes: parallel-plate loading test and three-edge bearing test, in which the effects of surrounding soil and distributed load on the pipe sample are ignored. However, in the available design methodologies the effect of bedding and load distribution is considered though empirical factors. As of today, there is no standard test method available for structural testing of pipes considering the effect of soil–pipe interaction system. Therefore, the objectives of this paper are to present a literature review of full-scale structural testing methods of relatively large diameter gravity pipes ranging from 36 in (90 cm) and larger, and suggest a general soil–pipe test procedure for structural evaluation of large diameter gravity pipes, such as culverts. Discussions are made for selection of a soil–pipe structural testing condition, loading method, loading rate, loading configurations, and required instrumentations for capturing and recording 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)0.001
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 score0.723
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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