Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10505 - Geology
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Results in Engineering
ISSN
2590-1230
e-ISSN
2590-1230
Volume of the periodical
17
Issue of the periodical within the volume
March
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
15
Pages from-to
100984
UT code for WoS article
000949879800001
EID of the result in the Scopus database
2-s2.0-85152912046