Version 1
: Received: 25 June 2024 / Approved: 25 June 2024 / Online: 25 June 2024 (14:23:02 CEST)
How to cite:
Cha, G.-W.; Park, C.-W. Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy. Preprints2024, 2024061781. https://doi.org/10.20944/preprints202406.1781.v1
Cha, G.-W.; Park, C.-W. Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy. Preprints 2024, 2024061781. https://doi.org/10.20944/preprints202406.1781.v1
Cha, G.-W.; Park, C.-W. Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy. Preprints2024, 2024061781. https://doi.org/10.20944/preprints202406.1781.v1
APA Style
Cha, G. W., & Park, C. W. (2024). Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy. Preprints. https://doi.org/10.20944/preprints202406.1781.v1
Chicago/Turabian Style
Cha, G. and Choon-Wook Park. 2024 "Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy" Preprints. https://doi.org/10.20944/preprints202406.1781.v1
Abstract
A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
