Effect of number and surface area of the aggregates on machine learning prediction performance of recycled hot-mix asphalt

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F24%3A00376329" target="_blank" >RIV/68407700:21110/24:00376329 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.conbuildmat.2024.137788" target="_blank" >https://doi.org/10.1016/j.conbuildmat.2024.137788</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.conbuildmat.2024.137788" target="_blank" >10.1016/j.conbuildmat.2024.137788</a>

Result language

angličtina

Original language name

Effect of number and surface area of the aggregates on machine learning prediction performance of recycled hot-mix asphalt

Original language description

This study addresses the challenge of designing hot-mix asphalt by evaluating the impact of aggregate surface area (ASA) and the number of aggregates (NA) in machine learning (ML) models. A dataset of 107 asphalt mixtures containing 0–50 % reclaimed asphalt pavement (RAP) was analyzed. Virgin aggregates and RAP particles were counted and measured via digital photography to calculate NA and ASA, with specific surface areas determined in a physics engine environment. Then, measured average aggregate particle weights were calibrated using 13 specimens. Various ML models were developed with the random forest algorithm, incorporating different input feature sets (IFS), including NA, ASA, and other basic features of the mixtures. Results revealed that including NA and ASA did not significantly improve model performance compared to using only gradation percentage inputs. Consequently, IFS-4, which includes only gradation inputs, was recommended for simplicity. The most crucial features were found to be gradation-related, with R(2) values around 0.90 and above achieved for stiffness modulus (ITSM), air voids, Marshall stability (MS), and theoretical maximum density (Gmm). Specifically, the test R(2) values for air voids, ITSM, and MS were 0.96, 0.89, and 0.87, with a mean absolute percentage error (MAPE) of 5.8 %, 5.4 %, and 5.6 %, respectively. Predictions for Gmm demonstrated the highest performance across all metrics with an R(2) value of 0.99. Air void content predictions performed better than those for ITSM, MS, and MF regarding R(2) and mean squared error (MSE) values, although their MAPE values were similar. These findings suggest that while NA and ASA provide additional details, gradation features are the most critical inputs for accurate ML model predictions in asphalt mixture design.

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

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Construction and Building Materials

ISSN

0950-0618

e-ISSN

1879-0526

Volume of the periodical

445

Issue of the periodical within the volume

září

Country of publishing house

GB - UNITED KINGDOM

Number of pages

25

Pages from-to

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UT code for WoS article

001297866900001

EID of the result in the Scopus database

2-s2.0-85201488750