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Record W2911085439 · doi:10.1139/cjce-2018-0531

Performance evaluation of jointed plain concrete pavements with sealed and unsealed joints in North Texas

2019· article· en· W2911085439 on OpenAlexvenueno aff
Mena I. Souliman, Ashish Tripathi, Lubinda F. Walubita, Mayzan Isied

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsSpallGeotechnical engineeringJoint (building)Infiltration (HVAC)GeologyForensic engineeringEngineeringCivil engineeringStructural engineeringMaterials science

Abstract

fetched live from OpenAlex

Joint sealing in jointed plain concrete pavement (JPCP) has been practiced throughout the world for many years as it improves the performance of concrete pavements. The infiltration of water is a common problem in concrete pavements and often increases distresses, such as faulting and pumping. For this reason, sealing the joints can help reduce water infiltration. Additionally, the infiltration of sand and small stones, aggregates, or debris into the joints can also be prevented, consequently reducing joint spalling in concrete pavements. However, it is also reported that joint sealing increases the initial cost of construction, especially if the joints need to be resealed, which leads to some additional costs. In this study, the pavement distress data was collected from the long-term pavement performance (LTPP) database for all the JPCPs sections in North Texas. The study illustrates the relative field performance in terms of spalling, faulting, roughness, and deflections of JPCP sections for both sealed and unsealed LTPP sections of North Texas.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.994

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 head0.006
GPT teacher head0.177
Teacher spread0.171 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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