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Record W3109057896 · doi:10.1155/2020/8868752

Microwave Heating Characteristics of Emulsified Asphalt Repair Materials Incorporated with Steel Slag

2020· article· en· W3109057896 on OpenAlexaff
Yongfeng Wei, Jinyang Huo, Zhenjun Wang, Jiangtao Gao

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsTransport Canada
FundersXinjiang Production and Construction CorpsU.S. Department of Transportation
KeywordsMaterials scienceAsphaltMicrowaveMicrowave heatingComposite materialSlag (welding)Metallurgy

Abstract

fetched live from OpenAlex

Emulsified asphalt needs to be cured for a certain age after demulsification to produce strength, which seriously affects the traffic opening time. In this work, microwave heating technology was applied for emulsified asphalt repair materials. Steel slag with high microwave activity was adopted to improve the performance of emulsified asphalt repair materials by microwave heating. Effects of steel slag sizes and contents on the heating rate, temperature distribution, and thermal performance of emulsified asphalt repair materials were analyzed by close microwave heating, open microwave heating, and repair simulation tests. Results show that the temperature of emulsified asphalt repair materials presents three different heating stages under microwave irradiation. The “critical point of phase transition” in the three stages is gradually advanced with the increase in steel slag content. The core temperature and maximum temperature of emulsified asphalt repair materials with different steel slag sizes are basically the same; however, the heat distribution of emulsified asphalt repair materials is significantly different. In contrast to conventional asphalt mixture, there exists a smaller temperature difference. The temperature of repairing materials can reach above 80°C. The interface area can form an embedded interface structure. Incorporation of steel slag and adoption of microwave heating are effective to improve the performance of emulsified asphalt repair materials.

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 score0.040
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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