Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F23%3A10480003" target="_blank" >RIV/00216208:11310/23:10480003 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=_Sl5jYdnCE" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=_Sl5jYdnCE</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2023.100984" target="_blank" >10.1016/j.rineng.2023.100984</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques

Popis výsledku v původním jazyce

Warm Mix Asphalts (WMAs) are asphalt concretes produced at lower temperatures than traditional Hot Mix Asphalts (HMAs). Nonetheless, the above is not enough to diminish the environmental impacts associated with the road infrastructure industry. Accordingly, incorporating Recycled Concrete Aggregate (RCA) as a partial replacement for Natural Aggregates (NAs) in WMA design has been gaining notoriety in the literature as a viable alternative to increase sustainability. However, the eco-friendly manufacturing of WMA with RCA contents (WMA-RCA) is not easy to obtain satisfactorily because the RCA causes alterations in the mix design. Thus, this research proposes three (3) methods to determine the optimal design conditions (coarse RCA content) that minimize the environmental burdens caused by WMA-RCA production. The first method is a mathematical model based on Multiple Linear Regression (MLR), which is used as a benchmark for the other two methods. The second and third methods are computational models based on Artificial Intelligence (AI), i.e., Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs), respectively. Notably, the Life Cycle Assessment (LCA) was employed as the theoretical framework to support all the proposed models. Consequently, this study concludes that: (i) all the proposed methodological alternatives achieve results with a great accuracy; (ii) the GAs model is the most precise method in terms of error minimization; (iii) the MLR model is the fastest method in terms of execution time; and (iv) the ANNs model is the method that requires the longest time of running, and its exactness is at a midpoint concerning the other models.

Název v anglickém jazyce

Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques

Popis výsledku anglicky

Warm Mix Asphalts (WMAs) are asphalt concretes produced at lower temperatures than traditional Hot Mix Asphalts (HMAs). Nonetheless, the above is not enough to diminish the environmental impacts associated with the road infrastructure industry. Accordingly, incorporating Recycled Concrete Aggregate (RCA) as a partial replacement for Natural Aggregates (NAs) in WMA design has been gaining notoriety in the literature as a viable alternative to increase sustainability. However, the eco-friendly manufacturing of WMA with RCA contents (WMA-RCA) is not easy to obtain satisfactorily because the RCA causes alterations in the mix design. Thus, this research proposes three (3) methods to determine the optimal design conditions (coarse RCA content) that minimize the environmental burdens caused by WMA-RCA production. The first method is a mathematical model based on Multiple Linear Regression (MLR), which is used as a benchmark for the other two methods. The second and third methods are computational models based on Artificial Intelligence (AI), i.e., Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs), respectively. Notably, the Life Cycle Assessment (LCA) was employed as the theoretical framework to support all the proposed models. Consequently, this study concludes that: (i) all the proposed methodological alternatives achieve results with a great accuracy; (ii) the GAs model is the most precise method in terms of error minimization; (iii) the MLR model is the fastest method in terms of execution time; and (iv) the ANNs model is the method that requires the longest time of running, and its exactness is at a midpoint concerning the other models.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10505 - Geology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

17

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

100984

Kód UT WoS článku

000949879800001

EID výsledku v databázi Scopus

2-s2.0-85152912046