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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Construction and Building Materials

ISSN

0950-0618

e-ISSN

1879-0526

Svazek periodika

445

Číslo periodika v rámci svazku

září

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

25

Strana od-do

—

Kód UT WoS článku

001297866900001

EID výsledku v databázi Scopus

2-s2.0-85201488750