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Record W78710397

Development of Concrete Link Slabs to Eliminate Bridge Expansion Joints Over Piers

2008· article· en· W78710397 on OpenAlexaboutno aff
Clifford Lam, David Lai, Jim Au, L. F. Lim, Wade Young, Bala Tharmabala

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsExpansion jointGirderSlabStructural engineeringDeckEngineeringDurabilityJoint (building)Bridge (graph theory)Forensic engineeringMaterials scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

A primary factor affecting the durability of bridge structures is the use of expansion joints at bridge support locations. The inability of current joint systems to provide reliable and effective long-term leakproof performance generally leads to early leakage of chloride-contaminated water through those joints, causing premature corrosion at girder ends and supporting substructures. The Ministry of Transportation of Ontario has tried to eliminate the expansion joints during deck rehabilitation by using a number of approaches. Some of the systems that have been implemented in recent projects include: (i) converting the simply-supported spans into a semi-continuous deck system by encasing the girder ends in a monolithic transverse concrete diaphragm, (ii) linking the simple spans by a thin concrete slab (flexible link slab) that spans between haunched beam sections on either side of link slab while keeping the girders discontinuous; and (iii) using a similar link slab concept except that the link slab is debonded from the girders for a longer length at each girder end. The paper discusses the relative merits of these systems from both technical and economic standpoints and provides examples of recent field applications of each system. The practical lessons that were learned and the field performance of each system to date are also presented.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.609

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)0.0010.000

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 head0.028
GPT teacher head0.235
Teacher spread0.207 · 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

Citations13
Published2008
Admission routes1
Has abstractyes

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