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Record W2754025611 · doi:10.5267/j.esm.2017.8.001

Evaluation of interfacial bond strength between Portland cement concrete and asphalt concrete layers using bi-material SCB test specimen

2017· article· en· W2754025611 on OpenAlexvenueno aff
MirMilad Mirsayar, Xijun Shi, Dan G. Zollinger

Bibliographic record

VenueEngineering Solid Mechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsPortland cementMaterials scienceComposite materialAsphaltAsphalt concreteBond strengthCementPolymer concreteBondStructural engineeringLayer (electronics)EngineeringAdhesive

Abstract

fetched live from OpenAlex

Portland cement concrete (PCC) / asphalt concrete (AC) bonded components are seen in both conventional pavement structures as well as overlays. Due to the environmental and traffic loads, cracks occur at the interface of the PCC and AC layers and finally, may propagate through the interface or one of the layers. Therefore, the evaluation of bond strength between these layers is important. This paper investigates bond strength between asphalt concrete and Portland cement concrete using a new sandwich test specimen. The developed specimen, called Bi-material semi-circular bend (BSCB) is made of asphalt concrete and the Portland cement concrete, cracked at the interface of the materials. First, the suggested specimen is introduced and characterized using finite element simulation. Then, the specimen is employed to obtain bond strength between asphalt concrete and Portland cement concrete under mixed mode loading, and at two temperatures: -20C and 20C. The fracture toughness at different mixed mode conditions is obtained, and finally, fracture criterion for the tested bonded joints is 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 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.039
GPT teacher head0.285
Teacher spread0.246 · 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 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

Citations100
Published2017
Admission routes1
Has abstractyes

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