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

Author:

Cha Gi-Wook1ORCID,Park Choon-Wook2,Kim Young-Chan3,Moon Hyeun Jun4

Affiliation:

1. School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu 41566, Republic of Korea

2. Industry Academic Cooperation Foundation, Kyungpook National University, Daegu 41566, Republic of Korea

3. Division of Smart Safety Engineering, Dongguk University Wise Campus, 123 Dongdae-ro, Gyeongju 38066, Republic of Korea

4. Department of Architectural Engineering, Dankook University, Yongin 16890, Republic of Korea

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 (R2) 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.

Funder

NATIONAL RESEARCH FOUNDATION OF KOREA (NRF) grant funded by the Korean Government

Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry, and Energy (MOTIE) of the Republic of Korea

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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