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Record W4391387168 · doi:10.1139/cjce-2023-0249

Influence of bonding condition of different interfaces on the mechanical responses and failure mode of asphalt pavement

2024· article· en· W4391387168 on OpenAlexvenueno aff
Kang Yao, Qiao Dong, Xueqin Chen, Xiang Wang, Shengjia Xie, Zhiqiang Cheng

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceAsphaltSubgradeFailure mode and effects analysisComposite materialUltimate tensile strengthAsphalt concreteAsphalt pavementTensile strainModulusFinite element methodStructural engineeringMode (computer interface)Geotechnical engineeringGeologyEngineeringComputer science

Abstract

fetched live from OpenAlex

In this study, a finite element model was established to compute the responses of asphalt pavement with various bonding conditions between the asphalt concrete layers (AC-AC interface) and between the asphalt concrete layer and base (AC-Base interface). The influence of bonding conditions of different interfaces on failure mode of asphalt pavement was discussed. The results indicated that poor bonding condition would increase longitudinal tensile strain at the bottom of the AC layers and vertical compressive strain at the top of subgrade, and the negative effects of two interfaces on pavement would enhance each other. It was observed that insufficient bonding strength would reduce the predicted life of pavement and, more importantly, debonding of AC-AC interface or two interfaces simultaneously would change pavement failure mode. Additionally, it should be noted that the fully-unbonded condition of AC-AC interface would affect the critical location for crack initiation in AC layers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.350

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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