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Record W2173686539

Bridge Designing Based on the New Combined Stretch-Shear Deformation Formula

2015· article· en· W2173686539 on OpenAlexvenueno aff
Shuanghua Huang, Wen-ba HAN, Bing-qing CAI, Jie Wang

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsShear stressStructural engineeringShear (geology)MechanicsStrength theoryEngineeringPoint (geometry)MathematicsClassical mechanicsContinuum mechanicsPhysicsGeometryGeology
DOInot available

Abstract

fetched live from OpenAlex

This paper discovered a phenomenon in which the mass point in unit cell cannot keep balance in current elastic theory. Under different stress states, the absolute values of all equilibrium stress on the mass point are greater than the absolute values of principal stress. Thus, based on new concept of point stress balance, this paper introduces the new formula of stretch-shear combined deformation. The new formula explains the issue that, in the state of stretch-shear, constructions destroy more easily than in the state of compress-shear. Besides, based on new concepts of point stress balance, this paper also establishes a new theory of strength that is much more accurate than the third and fourth strength theory, validated in the Damage Mechanics National Key Laboratory of Tsinghua University. Comparisons of experiment data show the errors calculated from the new theory are only 1%, while errors based on the third and fourth strength theory are 14.2% and 18.2%. Therefore, the author suggests using the new stretch-shear formula to solve problems in bridge engineering in the future.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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