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Record W4233766608 · doi:10.32920/ryerson.14657643

Development Of Precast Barrier Wall System For Bridge Decks

2021· preprint· en· W4233766608 on OpenAlexaffabout
G. N. Patel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPrecast concreteStructural engineeringEngineeringDeckBridge (graph theory)

Abstract

fetched live from OpenAlex

As Ontario transportation infrastructure enters the era of maintenance, rehabilitation and replacement, it needs more use of prefabricated elements and systems, which can quickly assembled to overcome relatively longer time to open bridge to the traffic. This thesis reports on both analytical and experimental investigations conducted on various aspects of precast bridge barrier, including design loads, structural design, sequence of construction, connection details between the barriers and bridge, joints between barrier segments and water leakage control. In this study, thru post-tensioned bolted connection system was developed to connect the precast barrier wall with bridge deck. The primary intension behind these connections is to expedite construction cycle simulataneously with minimizing traffic disruption, improve work-zone safety, quality and constructible and lower down lifecycle cost of bridges. A total of five experimental tests on full-scale specimens of the developed system were conducted to verify and substantiate the design procedure used to develop the barrier wall system. Good correlation between theoretical ultimate loads, in the form of bending, shear and punching shear resistance, and the experimental findings was achieved.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.244
Teacher spread0.217 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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