MétaCan
Menu
Back to cohort
Record W2232655754 · doi:10.1139/cjce-2015-0158

Estimating fatigue design load for overhead steel sign support structures under truck-induced wind pressure

2016· article· en· W2232655754 on OpenAlexafffundvenueabout
Han Hong, G.G. Zu, Jenny King

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWestern University
FundersMinistère des Transports
KeywordsTruckStructural engineeringRange (aeronautics)Reliability (semiconductor)EngineeringWind engineeringOverhead (engineering)Bridge (graph theory)Lateral earth pressureStress (linguistics)Marine engineeringAutomotive engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Experimental results of the truck-induced wind gust pressure were used as the basis to develop the equivalent static truck-induced wind pressure for fatigue design in the American Association of State Highway and Transportation Officials (AASHTO). The development does not explicitly quantify the stress range distribution, nor does the development discuss the implied reliability. No recommendation is given to consider truck-induced pressure for fatigue design in the Canadian Highway Bridge Design Code (CHBDC). This study quantifies the stress range due to truck traffic, and calibrates the equivalent static truck-induced wind pressure for fatigue design of overhead steel sign support structures. The reliability-based calibration is focused on the CHBDC. For the vertical excitations, the calibrated pressure is less than 50% of that suggested in the AASHTO. For the horizontal excitations, the calibrated pressure can be greater or smaller than that for the site-dependent natural wind gusts. Therefore, the truck-induced horizontal wind pressure could govern the fatigue design for some sites.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score0.810

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)0.0010.000

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 head0.023
GPT teacher head0.219
Teacher spread0.197 · 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

Citations7
Published2016
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicWind and Air Flow StudiesFrench-language works237,207