Volumetric Properties and Stiffness Modulus of Asphalt Concrete Mixtures Made with Selected Quarry Fillers: Experimental Investigation and Machine Learning Prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F23%3A00362847" target="_blank" >RIV/68407700:21110/23:00362847 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/ma16031017" target="_blank" >https://doi.org/10.3390/ma16031017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma16031017" target="_blank" >10.3390/ma16031017</a>

Result language

angličtina

Original language name

Volumetric Properties and Stiffness Modulus of Asphalt Concrete Mixtures Made with Selected Quarry Fillers: Experimental Investigation and Machine Learning Prediction

Original language description

In recent years, the attention of many researchers in the field of pavement engineering has focused on the search for alternative fillers that could replace Portland cement and traditional limestone in the production of asphalt mixtures. In addition, from a Czech perspective, there was the need to determine the quality of asphalt mixtures prepared with selected fillers provided by different local quarries and suppliers. This paper discusses an experimental investigation and a machine learning modeling carried out by a decision tree CatBoost approach, based on experimentally determined volumetric and mechanical properties of fine-grained asphalt concretes prepared with selected quarry fillers used as an alternative to traditional limestone and Portland cement. Air voids content and stiffness modulus at 15 °C were predicted on the basis of seven input variables, including bulk density, a categorical variable distinguishing the aggregates’ quarry of origin, and five main filler-oxide contents determined by means of X-ray fluorescence spectrometry. All mixtures were prepared by fixing the filler content at 10% by mass, with a bitumen content of 6% (PG 160/220), and with roughly the same grading curve. Model predictive performance was evaluated in terms of six different evaluation metrics with Pearson correlation and coefficient of determination always higher than 0.96 and 0.92, respectively. Based on the results obtained, this study could represent a forward feasibility study on the mathematical prediction of the asphalt mixtures’ mechanical behavior on the basis of its filler mineralogical composition.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20101 - Civil engineering

Project

<a href="/en/project/GF22-04047K" target="_blank" >GF22-04047K: Advanced approaches for determination and understanding of asphalt mix fatigue behaviour</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Name of the periodical

Materials

ISSN

1996-1944

e-ISSN

1996-1944

Volume of the periodical

16

Issue of the periodical within the volume

3

Country of publishing house

CH - SWITZERLAND

Number of pages

18

Pages from-to

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UT code for WoS article

000930195400001

EID of the result in the Scopus database

2-s2.0-85147794014