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Record W4382680808 · doi:10.11159/iccste23.126

The Position of Bitumen Emulsions on Different Bases

2023· article· en· W4382680808 on OpenAlexvenueno aff
Moritz Middendorf, Cristin Umbach, Stefan Böhm, Jia Liu, Bernhard Middendorf

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltComputer sciencePosition (finance)Materials scienceComposite materialBusiness

Abstract

fetched live from OpenAlex

Fast and durable repair of asphalt roads is important for a functioning infrastructure.An important method is the milling of the old asphalt layer in order to place a new layer on top ("hot on cold" paving).A bitumen emulsion is used as an adhesion primer.The adhesion of the materials due to the emulsion at the layer boundary is intended to improve the bond and thus dissipate the stresses that occur due to traffic loading [1]- [3].For this purpose, it is necessary to choose the correct quantity of spray [4]-[5] and to distribute it uniformly.Bitumen emulsion consists of bitumen, water and an emulsifier.The emulsifier causes the fine bitumen droplets to spread in a stable state in the water [6].After spraying, the bitumen emulsion breaks and the water evaporates [7].To determine the location of bitumen emulsion in this study, a C40 B5-S (40-wt.%content of bitumen) was sprayed onto the different bases using a specially designed spray-on system (spray-on quantities: 225 g/m 2 ; 450 g/m 2 ).The surfaces of the two variants differ in the texture of the surface and in the void content on the surface (dense surface (milled); open-pore surface (asphalt texture)).The aim of this study was to determine the position of bitumen emulsion on theses surfaces using the X-ray computed micro tomography (-CT) technique.Tis technique allows 3D imaging of structures by measuring different densities [8].For the analysis of the thin layers of the bitumen emulsion, a highdensity tracer (barium sulfate) was added, which had no interfering effect on the viscosity of the bitumen emulsion.This made it possible to identify the bitumen emulsion in the subsequent analysis due to the difference in density.[9].The (sprayed) samples were measured using a high-resolution -CT (type: Zeiss Xradia 520 Versa).The measurements were performed with a voltage of 140 kV and a power of 10 watts.The spatial resolution was 34.5 m.The results of the measurements with the -CT showed that the distribution of the bitumen emulsion on a milled surface is uneven.In the deepening's of the structure, an emulsion thickness of 917 m could be measured.Further there was only a small amount of bitumen emulsion on the crests and slopes compared to the deepening's (measured thickness: 217 m).For the samples with the asphalt surface the bitumen emulsion flows into the voids and was collected there.Almost no emulsion was detected at the actual layer boundary.From the results it can be concluded that the distribution is not optimal and thus no optimal bonding is achieved.For this reason, in further studies the bitumen emulsion will be adjusted in terms of viscosity to achieve a more uniform distribution.This study showed that a new understanding of the material can be obtained with 3D imaging, allowing optimization steps to be applied that lead to better material behavior.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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

Explore more

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