MétaCan
Menu
Back to cohort
Record W3116145910 · doi:10.18280/ijdne.150614

Mechanical System Evolution and Reasonable Structural Design Parameters of Long-Span Deck-Type Beam-Arch Composite Rigid Frame Bridge

2020· article· en· W3116145910 on OpenAlexvenueno aff
Yanchao Ding, Zhongfu Xiang, Yayong Li, Xuesong Zhang, Yin Zhou

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2020
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
FundersChina State Construction Engineering Corporation
KeywordsStructural engineeringArchDeflection (physics)Span (engineering)DeckBeam bridgeEngineeringBeam (structure)Rigid frameBending momentBending stiffnessFinite element methodStiffnessFrame (networking)Mechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Long-span deck-type beam-arch composite rigid frame (BACRF) bridge fully integrates the merits of arch bridges and beam bridges, and overcomes the cracking and deflection problems of continuous rigid frame bridges. As a perfect combination of beam bridges and arch bridges, the long-span deck-type BACRF bridge boasts a light structure, a strong bearing capacity, and a powerful spanning capability. From the perspective of mechanical system evolution, this paper theoretically analyzes the structural mechanics of the beam-arch composite system, establishes a half-bridge model for BACRF bridge, and derives the expressions of the internal force and displacement of the beam-arch composite system. Next, finite-element analysis was conducted to analyze how the variation of a single parameter, e.g., rise-span ratio, open web ratio, and side-to-middle span ratio, affects midspan displacement, arch-beam junction displacement, main beam bending moment, and main arch axial force. Finally, the calculation formula for deflection-span ratio of BACRF bridge was proposed based on the maximum hyperplane method. The research results provide a reference for the structural design of similar bridges.

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 score0.620
Threshold uncertainty score0.581

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 head0.013
GPT teacher head0.234
Teacher spread0.222 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicStructural Engineering and Vibration AnalysisFrench-language works237,207