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Record W4413641446 · doi:10.1617/s11527-025-02762-2

Article of RILEM TC 278-CHA: evaluation of self-healing properties of asphalt binders—outcomes and challenges

2025· article· en· W4413641446 on OpenAlexaff
Orazio Baglieri, Roberto M. Aurilio, Fabrizio Miglietta, Lucia Tsantilis, Hassan Baaj

Bibliographic record

VenueMaterials and Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Waterloo
FundersPolitecnico di Torino
KeywordsAsphaltSolid mechanicsMaterials scienceSelf-healingComposite materialForensic engineeringEngineeringMedicineAlternative medicine

Abstract

fetched live from OpenAlex

Abstract This paper describes part of the activities conducted by RILEM Technical Committee 278-CHA (Crack-Healing of Asphalt Pavement Materials) Task Group 2a. The main objective of the TG2a was to explore and evaluate the experimental methods and criteria for the assessment of self-healing of asphalt binders. The work was developed in two phases. In the first phase, various existing healing test protocols were scrutinized, encompassing time-sweeps with single rest periods (TS-SRP), time-sweeps with multiple rest periods (TS-MRP), and linear amplitude sweeps with rest periods LASH). The protocols were evaluated and compared across different laboratories. The outcomes of interlaboratory testing led to the selection of the LASH-based approach. However, it was acknowledged that new insights and modifications were necessary due to the possible occurrence of biasing effects during the amplitude sweep. Such issues were addressed in the second phase of research, in which a modified LASH-based procedure underwent further investigation using various methods for data analysis, including approaches based on dissipated energy and the viscoelastic continuum damage model.

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)0.001
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 score0.140
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.040
GPT teacher head0.265
Teacher spread0.226 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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