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Record W4387664690 · doi:10.1139/cjce-2023-0219

Network-level evaluation method of the overall–intralayer–interlayer interface structural states of semi-rigid asphalt pavement

2023· article· en· W4387664690 on OpenAlexvenueno aff
Jianwei Fan, Tao Ma, Yajing Zhu, Yiming Zhang

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSubgradeAsphaltStructural engineeringAsphalt pavementIndex (typography)Computer scienceMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

The pavement structure strength index (PSSI) indicator in the Chinese standard and the structural number (SN) indicator proposed by AASHTO are widely used to evaluate the structural states of semi-rigid asphalt pavement. However, PSSI and SN indicators do not involve the intra- and interlayer interface structural state, and the indicators are disconnected from the pavement design indicators. In this study, the intralayer remaining life and structural attenuation state were considered comprehensively to propose the indicators of the surface/base/subgrade structural state index (SSSI/BSSI/GSSI). The interlayer interface structural state index (ISSI) was proposed based on the friction coefficient between the surface and base layers. The pavement structural state index (PaSSI) was proposed by assigning the weightings to SSSI, BSSI, GSSI, and ISSI indicators. Compared with the PSSI or SN indicators, the proposed indicators realize the intralayer–interlayer–overall structural evaluation of the pavement, and the indicators were based on the pavement design indicators.

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)0.002
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 score0.092
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.027
GPT teacher head0.268
Teacher spread0.241 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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