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Record W4404799504 · doi:10.1061/jbenf2.beeng-7073

Case Study of Long-Term Bearing and Expansion Joint Movement on a Prestressed Concrete Bridge

2024· article· en· W4404799504 on OpenAlexaffabout
Isabel Heykoop, Joshua E. Woods, Neil A. Hoult, Millar Coveney

Bibliographic record

VenueJournal of Bridge Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsQueen's University
Fundersnot available
KeywordsPrestressed concreteExpansion jointBridge (graph theory)Structural engineeringBearing (navigation)Joint (building)EngineeringTerm (time)Forensic engineeringGeotechnical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Bearings and expansion joints are critical elements of many bridges that permit deformations of the superstructure due to creep, shrinkage, and changes in temperature to occur without the development of longitudinal stresses. Despite their importance in ensuring the functionality and longevity of bridge structures, there is a lack of data in the literature on the long-term performance of bridge bearings and expansion joints, especially under harsh environmental conditions. This paper presents the results of a long-term monitoring campaign on the bearing and expansion joint movement on the Waaban Crossing bridge in Kingston, Ontario, Canada. The bridge movement was measured using a low-cost microelectromechanical system and Internet of things-based monitoring system. The study results revealed that the monitored bridge structure was behaving as expected and that the observed long-term trends in the movement of the bearings and expansion joints were in agreement with the assumptions made in the design of the bridge. The paper also discusses the challenges surrounding the creation of a robust and reliable monitoring system in harsh climatic conditions.

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 score0.308
Threshold uncertainty score0.971

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 head0.025
GPT teacher head0.250
Teacher spread0.224 · 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
Published2024
Admission routes2
Has abstractyes

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