IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i16p7064-d1458343.html
   My bibliography  Save this article

Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy

Author

Listed:
  • Gi-Wook Cha

    (Academic-Research Digital Convergence Scale-Up Platform Center, 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, Gyeongju 38066, Republic of Korea)

Abstract

A suitable waste-management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and it requires precise data on the volume of demolition waste (DW) generated. Therefore, we developed an optimal machine learning model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. We constructed a dataset comprising information on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, landfill mix waste, and recyclable minerals). We applied an artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest, and support vector regression. Further, we derived the optimal model through data preprocessing, input variable selection, and hyperparameter tuning. In both the validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies (R 2 ) of 0.987 and 0.972, respectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies (R 2 ) of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. This study confirmed through Shapley Additive exPlanations analysis that the floor area is the most important input variable in the four models (i.e., recyclable mineral waste, recyclable combustible waste, recyclable metals, and landfill mix waste). Additionally, the type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill waste generated. The results of this study can provide more detailed information on the generation of recycling and landfill waste. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

Suggested Citation

  • Gi-Wook Cha & Choon-Wook Park & Young-Chan Kim, 2024. "Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy," Sustainability, MDPI, vol. 16(16), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7064-:d:1458343
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/16/7064/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/16/7064/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gi-Wook Cha & Hyeun-Jun Moon & Young-Chan Kim, 2021. "Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables," IJERPH, MDPI, vol. 18(16), pages 1-16, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2022. "Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
    2. Gi-Wook Cha & Won-Hwa Hong & Young-Chan Kim, 2023. "Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    3. Gi-Wook Cha & Won-Hwa Hong & Se-Hyu Choi & Young-Chan Kim, 2023. "Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    4. Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2023. "Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    5. Nehal Elshaboury & Abobakr Al-Sakkaf & Eslam Mohammed Abdelkader & Ghasan Alfalah, 2022. "Construction and Demolition Waste Management Research: A Science Mapping Analysis," IJERPH, MDPI, vol. 19(8), pages 1-25, April.
    6. Junyoung Jeong & Keuntae Cho, 2024. "Proposing Machine Learning Models Suitable for Predicting Open Data Utilization," Sustainability, MDPI, vol. 16(14), pages 1-23, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7064-:d:1458343. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.