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Construction Waste Modeling for the Republic of Serbia

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Title Construction Waste Modeling for the Republic of Serbia
 
Creator Čantrak, Miroslav
Micić, Darko
Prokić, Dunja
Pezo, Lato
Ćurčić, Ljiljana
Mladenović, Marija
 
Subject Artificial neural network
Construction waste prediction
Data modeling techniques
Support vector machine
Sustainable construction
 
Description 557-666
The management of Construction Waste (CW) presents a significant challenge in sustainable development efforts. This
study employs data modeling techniques to predict the annual quantities of various types of construction waste,
encompassing total waste, metal waste, plastic waste, wood waste, mineral waste, and soil/concrete waste (CW1–CW8,
respectively). The study has mainly focused on reusable construction waste. For this purpose, 7 models were developed for
each type of construction waste – 5 polynomials (from the first to the fifth degree), Artificial Neural Network (ANN) and
Support Vector Machine (SVM) models. The ANN models were found to be the most effective for all types of CW
compared to the other models developed. The ANN models developed for CW1, CW6 and CW7 had a high R2 value
(> 0.85), indicating their potential for predicting the amount of these types of CW in the future. The ANN models developed
for the remaining types of CW had weaker performance (R2 < 0.60), but their performance could be improved in the future
investigations with an increase in the amount of data on CW generation. This research underscores the importance of
employing advanced data modeling techniques in addressing the challenges of construction waste management. By
providing accurate predictions of CW generation, stakeholders can better strategize waste reduction, recycling, and disposal
efforts, thereby contributing to the sustainable development goals of minimizing environmental impact and promoting
resource efficiency in the construction sector.
 
Date 2024-05-06T11:05:48Z
2024-05-06T11:05:48Z
2024-05
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63851
https://doi.org/10.56042/jsir.v83i5.2474
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.83(5) [May 2024]