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Record W3189179677 · doi:10.5267/j.esm.2021.7.001

Development of a nomogram to predict the contact stress between an I-girder and a support roller

2021· article· en· W3189179677 on OpenAlexvenueno aff
Thanin Chanmalai, Byungik Chang, Kevin Misaro, Saron Hagos, Thippesh Bethur Hanumanthareddy

Bibliographic record

VenueEngineering Solid Mechanics · 2021
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGirderStructural engineeringNomogramBridge (graph theory)Finite element methodParametric statisticsContact mechanicsStress (linguistics)EngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

The incremental launching method (ILM) is an efficient method of bridge construction primarily suited for environmentally sensitive areas. However, during the bridge launching, there are significant contact stresses between the launching system and the steel bridge girders. These substantial contact stresses can cause damage both on, and just under, the girder surface. Although Hertz contact theory solutions may give an insight into the problem, the accuracy is uncertain due to the presence of complex geometries, loads, and material properties. The complicated structural systems need to rely on numerical modeling such as the finite element analysis which are not always available. The primary objective of this study is to estimate the relationship of the maximum contact stress between an I-girder and a roller using a nomogram. The nomogram is built based on a parametric study with various roller dimensions and loads by numerical modeling. The maximum contact stress from the nomogram can be a useful tool in designing a bridge girder on a support roller.

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 score0.501
Threshold uncertainty score0.945

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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