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Record W3005689199 · doi:10.1139/cjce-2019-0395

Application of nanotechnology in pavement engineering: a review

2020· review· en· W3005689199 on OpenAlexvenueno aff
Peerzada Mosir Shah, Mohammad Shafi Mir

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typereview
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltNanotechnologyApplications of nanotechnologyNanomaterialsEngineeringMaterials science

Abstract

fetched live from OpenAlex

Nanotechnology utilizes the research and technology established at the nuclear and molecular or macromolecular levels to comprehend the nanoscale phenomena. In road construction, the bituminous materials are used in enormous amounts. The macroscopic mechanical conduct of these materials still largely relies on microstructure and physical characteristics at a micro and nano scale level. For many years, scientists and engineers have been exploring nanotechnology in civil engineering, but nanotechnology use in this sector has been limited. Nanotechnology has caught the interest of experts and has progressively penetrated into the field of pavement engineering. The article reviews the published works carried out to study the use of nanomaterials in asphalt pavements and illustrates the technique used to enhance asphalt features by the use of these nanomaterials. In this review, multiple nanomaterials are discussed followed by characterization of these nanomaterials, preparation techniques of modified asphalt binder, and finally the effect of nanomaterials on the efficiency of base asphalt binder as well as the asphalt mixture. The primary focus continues on the use of nanotechnology in pavement engineering to achieve a better atmosphere for society based on recognized pavement engineering requirements and difficulties.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2020
Admission routes1
Has abstractyes

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