Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate

Gi-Wook Cha, Won-Hwa Hong, Se-Hyu Choi and Young-Chan Kim ()
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Gi-Wook Cha: School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Won-Hwa Hong: School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
Se-Hyu Choi: School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, 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

Sustainability, 2023, vol. 15, issue 13, 1-20

Abstract: Smart management of construction and demolition (C&D) waste is imperative, and researchers have implemented machine learning for estimating waste generation. In Korea, the management of demolition waste (DW) is important due to old buildings, and it is necessary to predict the amount of DW to manage it. Thus, this study employed decision tree (DT)-based ensemble models (i.e., random forest—RF, extremely randomized trees—ET, gradient boosting machine—GBM), and extreme gradient boost—XGboost) based on data characteristics (i.e., small datasets with categorical inputs) to predict the demolition waste generation rate (DWGR) of buildings in urban redevelopment areas. As a result of the study, the RF and GBM algorithms showed better prediction performance than the ET and XGboost algorithms. Especially, RF (6 features, 450 estimators; mean, 1169.94 kg·m −2 ) and GBM (4 features, 300 estimators; mean, 1166.25 kg·m −2 ) yielded the top predictive performances. In addition, feature importance affecting DWGR was found to have a significant impact on the order of gross floor area (GFA) > location > roof material > wall material. The straightforward collection of features used here can facilitate benchmarking as a decision-making tool in demolition waste management plans for industry stakeholders and policy makers. Therefore, in the future, it is required to improve the predictive performance of the model by updating additional data and building a reliable dataset.

Keywords: machine learning; random forest; extreme gradient boost; construction and demolition; waste generation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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