Performance Prediction of Fine-Grained Asphalt Concretes with Different Quarry Fillers by Machine Learning Approaches

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F21%3A00361176" target="_blank" >RIV/68407700:21110/21:00361176 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1088/1757-899X/1203/2/022113" target="_blank" >https://doi.org/10.1088/1757-899X/1203/2/022113</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1757-899X/1203/2/022113" target="_blank" >10.1088/1757-899X/1203/2/022113</a>

Result language

angličtina

Original language name

Performance Prediction of Fine-Grained Asphalt Concretes with Different Quarry Fillers by Machine Learning Approaches

Original language description

In general terms, an artificial neural network is a distributed processor that consists of elementary computational units interconnected. Such structure is inspired by the functioning principles of the biological nervous system and has proven to be effective in identifying complex relationships between an assigned input features vector and an experimental-investigated target vector for any scientific problem. The current paper represents a forward feasibility study on predicting the mechanical response of asphalt concretes prepared with different quarry fillers used as alternatives for traditional limestone filler or Portland cement by Machine Learning approaches which consider the chemical properties of the selected fillers and the quarry aggregate types as input variables. In fact, the case study involved several fillers and stone aggregates that were used to produce Marshall specimens of a specific fine-grained asphalt concretes designed originally for the assessment of filler suitability in terms of adhesion phenomenon. The asphalt concrete variants had the same material composition and mix design: all specimens were compacted by 2x50 blows using impact compactor, filler content was fixed at 10% by mass of the mix, the grading curve is roughly the same, the employed bitumen has a 160/220 penetration grade and is about 6% by mass of the mix. The mineralogical composition was investigated by X-ray fluorescence spectrophotometry tests. It represents a non-destructive laboratory analysis that allowed to specify and compare the main oxides composition associated with the employed natural fillers to be identified. Based on the results thus obtained and applying a categorical variable that distinguishes the stone aggregate type, a neural model has been developed that can predict the stiffness modulus of asphalt mixtures: therefore, this study presents a possible procedure for the development of predictive models that can help or improve the mix design process, when different fillers and stone aggregates are available.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

20101 - Civil engineering

Project

<a href="/en/project/GA18-13830S" target="_blank" >GA18-13830S: Comprehensive study on physicochemical interaction and related phenomena between bitumen and mineral aggregate by advanced experimental methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IOP Conference Series: Materials Science and Engineering

ISSN

1757-8981

e-ISSN

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Volume of the periodical

1203

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

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