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Record W4387325106 · doi:10.1139/cgj-2023-0055

Design-truck ultimate limit states of buried modular polymer stormwater collection structures

2023· article· en· W4387325106 on OpenAlexafffundvenue
James LeBlanc, R.W.I. Brachman, A. Zarpeima

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringBending momentBucklingTruckLimit state designModular designBendingMoment of inertiaGeotechnical engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Modular polymer stormwater structures are a relatively new class of buried structure with two horizontal platens supported by vertical columns. While structural demand at design truck loads has been quantified, the ultimate limit state to design truck loading is unknown. Full-scale physical experiments were conducted to quantify the applied load and identify the mode(s) of failure at the ultimate limit state of three types of modular structures when buried (with 0.6 m soil cover) and subject to static design truck loading. All three module types tested reached an ultimate limit state at applied loads around three times the nominal design load but had different modes of failure. The ultimate resistance for the module with columns having the lowest moment of inertia relative to length squared was caused by column buckling and top platen rupture, while the two modules with nearly twice the moment of inertia relative to length squared failed by top platen rupture and their column forces at most were 70%–75% of their short-term single column buckling resistance. The results provide new data to allow independent load rating of the buried structure, support continued assessment of column buckling, and identify the need to limit platen bending deflections.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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.

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

Citations2
Published2023
Admission routes3
Has abstractyes

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