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Record W2982500455 · doi:10.1139/cjce-2019-0288

Utilization of water-quenching blast furnace slag as alternative filler in asphalt mastic

2019· article· en· W2982500455 on OpenAlexvenueno aff
Jianyou Huang, Xiangyang Xing, Jun Cai, Jianzhong Pei, Rui Li, Yong Wen

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersChina Postdoctoral Science Foundation
KeywordsAsphaltMaterials scienceFiller (materials)CreepComposite materialRutRheologySweep frequency response analysisViscosityQuenching (fluorescence)Ground granulated blast-furnace slagFly ash

Abstract

fetched live from OpenAlex

Water-quenching blast furnace slag as a by-product of the iron production has caused serious environmental concerns. This paper tried to investigate the feasibility of the blast furnace slag filler (WBFSF) used as an alternative filler to replace the limestone filler (LF) in asphalt mixtures. Specifically, the chemical compositions, morphology characteristics, phase distributions, and thermal properties of two fillers were studied; then rheological properties of asphalt mastics in four filler–asphalt ratios were further studied by the rotational viscosity, temperature sweep, temperature–frequency–sweep (T-f-sweep), and multiple stress creep recovery (MSCR) test. The results show that WBFSF has a larger specific surface area and better-developed mesopores compared with LF. WBFSF asphalt mastic presents a larger complex modulus and a smaller phase angle. Moreover, the MSCR results show that WBFSF improves the elastic recovery and rutting resistance of asphalt mastics. Therefore, WBFSF presents great potential to be used as an alternative filler in asphalt mixtures.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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