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Record W4415848562 · doi:10.1680/jstbu.25.00084

Mechanical response of bridge deck pavement structural layer under rubber tyre load

2025· article· en· W4415848562 on OpenAlexaff
Xiangyang Lv, Zhanyou Yan, Guofang Zhao

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Structures and Buildings · 2025
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsSpan (engineering)Natural rubberBridge (graph theory)Vertical displacementDisplacement (psychology)Stress (linguistics)Deck

Abstract

fetched live from OpenAlex

To study the mechanical relationship between vehicles and bridge decks, a bridge model, a rubber tyre model and a bridge deck pavement (BDP) model were constructed based on vehicle–bridge dynamic coupling theory. The rationality of the rubber tyre model was verified, and the rubber tyre and bridge models were coupled. The research results indicate that, regarding the maximum vertical displacement in the middle of spans, first span > second span > third span. For the equivalent stress in the middle of spans, first span > second span > third span. For the transverse stress in the middle of spans, first span < second span < third span. For the longitudinal stress in the middle of spans, second span > third span > first span. With an increase in the depth of the bridge deck, the vertical displacement of the BDP gradually decreased. With an increase in vehicle speed, the vertical displacement in the middle of the bridge span decreased. Therefore, the rubber tyre model using the discrete-element method can accurately simulate vehicle loads.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.666

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

Explore more

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