Predicting volumetric properties and mechanical characteristics of asphalt concretes for thin wearing layers using a categorical boosting model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F24%3A00375578" target="_blank" >RIV/68407700:21110/24:00375578 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1201/9781003402541-108" target="_blank" >https://doi.org/10.1201/9781003402541-108</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1201/9781003402541-108" target="_blank" >10.1201/9781003402541-108</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting volumetric properties and mechanical characteristics of asphalt concretes for thin wearing layers using a categorical boosting model

Popis výsledku v původním jazyce

In recent years, new modeling strategies based on data-driven approaches are gaining increas-ing popularity in the field of pavement engineering. This study is aimed at developing a novel predictive model based on a supervised categorical boosting (CatBoost) algorithm that allows volumetric properties and mechanical characteristics of asphalt concretes (ACs) for thin wear-ing layers to be simultaneously predicted. The research involved 92 AC specimens produced both in laboratory and in plant with two different types of bitumen: a conventional and a modified one. In particular, air voids content, voids in the mineral aggregate, and stiffness modulus at 20°C were successfully correlated to bitumen content, particle size parameters and a categorical variable distinguishing the mixture production site and the binder type. The best model hyperparameters were accurately determined, and several performance metrics were evaluated to confirm the remarkable predictive capabilities achieved by the developed machine learning model.

Název v anglickém jazyce

Predicting volumetric properties and mechanical characteristics of asphalt concretes for thin wearing layers using a categorical boosting model

Popis výsledku anglicky

In recent years, new modeling strategies based on data-driven approaches are gaining increas-ing popularity in the field of pavement engineering. This study is aimed at developing a novel predictive model based on a supervised categorical boosting (CatBoost) algorithm that allows volumetric properties and mechanical characteristics of asphalt concretes (ACs) for thin wear-ing layers to be simultaneously predicted. The research involved 92 AC specimens produced both in laboratory and in plant with two different types of bitumen: a conventional and a modified one. In particular, air voids content, voids in the mineral aggregate, and stiffness modulus at 20°C were successfully correlated to bitumen content, particle size parameters and a categorical variable distinguishing the mixture production site and the binder type. The best model hyperparameters were accurately determined, and several performance metrics were evaluated to confirm the remarkable predictive capabilities achieved by the developed machine learning model.

Druh

D - Stať ve sborníku

CEP obor

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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 statě ve sborníku

Bituminous Mixtures and Pavements VIII

ISBN

978-1-003-40254-1

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

919-927

Název nakladatele

CRC Press/Balkema

Místo vydání

Leiden

Místo konání akce

Thessaloniki

Datum konání akce

12. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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