Neural networks implementation for the environmental optimisation of the recycled concrete aggregate inclusion in warm mix asphalt
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F24%3A10480823" target="_blank" >RIV/00216208:11310/24:10480823 - isvavai.cz</a>
Výsledek na webu
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=IQQVmJhDJG" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=IQQVmJhDJG</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/14680629.2023.2230298" target="_blank" >10.1080/14680629.2023.2230298</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Neural networks implementation for the environmental optimisation of the recycled concrete aggregate inclusion in warm mix asphalt
Popis výsledku v původním jazyce
Regarding the traditional Hot Mix Asphalt (HMA), Warm Mix Asphalt (WMA) with Recycled Concrete Aggregate (RCA) contents (WMA-RCA) requires lower production temperatures and diminishes the consumption of natural aggregates (NAs). Nonetheless, these environmental benefits may be counteracted by the higher optimal asphalt binder demanded by the WMA-RCAs. In this regard, this research develops a computational model to optimize the WMA-RCA design. In order to build a sufficiently accurate and adaptable model, it was decided to employ Artificial Neural Networks (ANNs). The ANN implementation was based on the postulates of the statistical learning theory, i.e., preferring to generate learning through low-complexity models. Also, a representative case study of the northern region of Colombia was assessed. In this scenario, the optimal coarse RCA content was 10%, and the sustainability savings were maintained up to an RCA's hauling distance of 200 km.
Název v anglickém jazyce
Neural networks implementation for the environmental optimisation of the recycled concrete aggregate inclusion in warm mix asphalt
Popis výsledku anglicky
Regarding the traditional Hot Mix Asphalt (HMA), Warm Mix Asphalt (WMA) with Recycled Concrete Aggregate (RCA) contents (WMA-RCA) requires lower production temperatures and diminishes the consumption of natural aggregates (NAs). Nonetheless, these environmental benefits may be counteracted by the higher optimal asphalt binder demanded by the WMA-RCAs. In this regard, this research develops a computational model to optimize the WMA-RCA design. In order to build a sufficiently accurate and adaptable model, it was decided to employ Artificial Neural Networks (ANNs). The ANN implementation was based on the postulates of the statistical learning theory, i.e., preferring to generate learning through low-complexity models. Also, a representative case study of the northern region of Colombia was assessed. In this scenario, the optimal coarse RCA content was 10%, and the sustainability savings were maintained up to an RCA's hauling distance of 200 km.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10505 - Geology
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Road Materials and Pavement Design
ISSN
1468-0629
e-ISSN
2164-7402
Svazek periodika
25
Číslo periodika v rámci svazku
5
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
26
Strana od-do
941-966
Kód UT WoS článku
001022744200001
EID výsledku v databázi Scopus
2-s2.0-85164490002