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Record W6976713822 · doi:10.60692/vkscj-8sg53

Structural Qualification of a Developed GFRP-Reinforced Concrete Bridge Barrier using Ultimate Load Testing

2023· article· en· W6976713822 on OpenAlexaffabout

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPunchingDurabilityBridge (graph theory)Shear (geology)Flexural strengthCorrosionFibre-reinforced plasticUltimate loadShear strength (soil)

Abstract

fetched live from OpenAlex

Corrosion of reinforcing steel bars is the main factor affecting durability and service life of steel-reinforced bridge barriers in North America. The use of glass fiber reinforcing polymer (GFRP) bars as non-corrosive material has emerged as an innovative solution to corrosion related problems. A recent cost-effective design of PL-3 bridge barrier was developed at Ryerson University incorporating high-modulus GFRP bars with headed ends. An experimental program was conducted to investigate the load carrying capacity of the developed barrier wall. A 40-m long barrier was constructed and tested at four different locations to investigate its structural behavior, crack pattern and ultimate strength when subjected to the equivalent static loading simulating vehicle impact. Experimental results were compared with the design values specified in the Canadian Highway Bridge Design Code. Experimental findings showed a large margin of safety for the proposed GFRP-reinforced barriers. The failure pattern due to transversal loading the longitudinal barrier over 2400 mm length was initiated by a trapezoidal flexural crack pattern at front face of the barrier, followed by punching shear failure at the transverse load location. Comparison between the available punching shear equations in the literature and the punching shear failure developed in the barrier wall was conducted.

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: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.250
Teacher spread0.169 · 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
Published2023
Admission routes2
Has abstractyes

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