Stiffness Data of High-Modulus Asphalt Concretes for Road Pavements: Predictive Modeling by Machine-Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F22%3A00354215" target="_blank" >RIV/68407700:21110/22:00354215 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/coatings12010054" target="_blank" >https://doi.org/10.3390/coatings12010054</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/coatings12010054" target="_blank" >10.3390/coatings12010054</a>

Result language

angličtina

Original language name

Stiffness Data of High-Modulus Asphalt Concretes for Road Pavements: Predictive Modeling by Machine-Learning

Original language description

This paper presents a study about a Machine Learning approach for modeling the stiffness of different high-modulus asphalt concretes (HMAC) prepared in the laboratory with harder paving grades or polymer-modified bitumen which were designed with or without reclaimed asphalt (RA) content. Notably, the mixtures considered in this study are not part of purposeful experimentation in support of modeling, but practical solutions developed in actual mix design processes. Since Machine Learning models require a careful definition of the network hyperparameters, a Bayesian optimization process was used to identify the neural topology, as well as the transfer function, optimal for the type of modeling needed. By employing different performance metrics, it was possible to compare the optimal models obtained by diversifying the type of inputs. Using variables related to the mix composition, namely bitumen content, air voids, maximum and average bulk density, along with a categorical variable that distinguishes the bitumen type and RAP percentages, successful predictions of the Stiffness have been obtained, with a determination coefficient (R2) value equal to 0.9909. Nevertheless, the use of additional input, namely the Marshall stability or quotient, allows the Stiffness prediction to be further improved, with R2 values equal to 0.9938 or 0.9922, respectively. However, the cost and time involved in the Marshall test may not justify such a slight prediction improvement.

Czech name

—

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

—

OECD FORD branch

20101 - Civil engineering

Project

<a href="/en/project/TE01020168" target="_blank" >TE01020168: Centre for Effective and Sustainable Transport Infrastructure (CESTI)</a><br>

Continuities

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

Publication year

2022

Confidentiality

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

Name of the periodical

Coatings

ISSN

2079-6412

e-ISSN

2079-6412

Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

20

Pages from-to

1-20

UT code for WoS article

000746141800001

EID of the result in the Scopus database

2-s2.0-85122335427