Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks

Gi-Wook Cha, Choon-Wook Park (), Young-Chan Kim and Hyeun Jun Moon
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Gi-Wook Cha: School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Choon-Wook Park: Industry Academic Cooperation Foundation, Kyungpook National University, Daegu 41566, Republic of Korea
Young-Chan Kim: Division of Smart Safety Engineering, Dongguk University Wise Campus, 123 Dongdae-ro, Gyeongju 38066, Republic of Korea
Hyeun Jun Moon: Department of Architectural Engineering, Dankook University, Yongin 16890, Republic of Korea

Sustainability, 2023, vol. 15, issue 23, 1-22

Abstract: In South Korea, demolition waste (DW) management has become increasingly significant owing to the rising number of old buildings. Effective DW management requires an efficient approach that accurately quantifies and predicts the generation of DW (DWG) of various types, which necessitates access to the required information or technology capable of achieving this. Hence, we developed an artificial intelligence-based model that predicts the generation of ten DW types, specifically from buildings in redevelopment areas. We used an artificial neural network algorithm with <10 neurons in the hidden layer to derive individual input variables and optimal hyperparameters for each DW type. All DWG prediction models achieved an average validation and test prediction performance (R 2 ) of 0.970 and 0.952, respectively, with their ratios of percent deviation ≥ 2.5, verifying them as excellent models. Moreover, Shapley additive explanations analysis revealed that DWG was most impacted by the floor area for all DW types, with a positive correlation with DWG. Conversely, other factors showed either a positive or negative correlation with DWG, depending on the DW type. The study findings may assist demolition companies and local governments in making informed decisions for efficient DW management and resource allocation by accurately predicting the generation of various types of DW.

Keywords: waste management; demolition waste generation; machine learning; artificial neural network; SHAP analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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