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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/GA18-13830S" target="_blank" >GA18-13830S: Podrobný výzkum fyzikálně-chemické interakce a souvisejících jevů mezi asfaltem a kamenivem pomocí pokročilých experimentálních metod</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

IOP Conference Series: Materials Science and Engineering

ISSN

1757-8981

e-ISSN

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Svazek periodika

1203

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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