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Record W3162781421 · doi:10.18280/acsm.450211

Physical Properties and Durability of Green Fiber-Reinforced Concrete for Road Bridges

2021· article· en· W3162781421 on OpenAlexvenueno aff
Chenglong Ma

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsDurabilityShrinkageMaterials scienceFlexural strengthCarbonationUltimate tensile strengthCrackingCompressive strengthStructural engineeringBrittlenessComposite materialFiberFiber-reinforced concreteEngineering

Abstract

fetched live from OpenAlex

Dry shrinkage and brittleness are outstanding problems with the concrete in road bridges. However, the road bridge concrete has not been systematically studied. No research has fully considered the influence of age on the physical-mechanical properties of road bridge concrete. There are few reports on how the green fiber dosage affects road bridge concrete. This paper analyzes the physical properties and durability of green fiber-reinforced concrete for road bridges (RBGFRC). Firstly, calculation methods were provided for physical-mechanical properties of RBGFRC, such as crack resistance, shear capacity of oblique section, and bending capacity of normal section. Next, the physical-mechanical properties of RBGFRC were investigated in terms of flexural strength, compressive strength, splitting tensile strength, and early cracking strength; the durability of the material was discussed from three aspects: carbonation resistance, resistance to freeze-thaw cycles, and porosity. Experimental results verify the good physical properties and durability of RBGFRC. The research provides a reference for applying RBGFRC in other scenarios.

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)0.001
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 score0.088
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.049
GPT teacher head0.264
Teacher spread0.215 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicInnovative concrete reinforcement materialsFrench-language works237,207