대한건축학회논문집 (Journal of the Architectural Institute of Korea)

  • 제39권2호
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  • Pages.229-235
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  • 2023
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  • 2733-6239(pISSN)
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  • 2733-6247(eISSN)
대한건축학회 (Architectural Institute of Korea)
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해체폐기물 발생량 예측을 위한 머신러닝 모델 개발

Development of Machine Learning Predictive Model for Forecasting Demolition Waste Generation

  • 차기욱 (경북대학교 과학기술실용공학부) ;
  • 홍원화 (경북대학교 건설환경에너지공학부)
  • Cha, Gi-Wook (School of Science and Technology Acceleration Engineering, Kyungpook National University) ;
  • Hong, Won-Hwa (School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University)
  • 투고 : 2022.11.02
  • 심사 : 2023.01.09
  • 발행 : 2023.02.28
⟨ 이전 논문 다음 논문 ⟩

초록

Due to the rapid increase in Construction & Demolition (C&D) waste, C&D waste management (WM) management is an important challenge, and Artificial Intelligence (AI) technology is being actively used as a smart WM strategy. Demolition waste (DW) predictive models were developed and tested by applying artificial neural network (ANN) and support vector machine (SVM) based on a dataset consisting of categorical input variables in this study. For this, DW predictive models with optimal performance were derived through hyper-parameter tuning of ANN and SVM algorithms. As a result of this study, the predictive performance of the ANN and SVM models showed mean absolute error (MAE) 71.730 and 79.437, root mean square error (RMSE) 119.414 and 104.979, coefficient of determination (R squared) 0.891 and 0.859 mean square error (MSE) 11020.556 and 14259.820 respectively. Therefore, the ANN model was confirmed to be a better model for predicting the DW than the SVM model in this study. At this time, the mean of the observed values is 987.181 kg·m-2 , and the mean of the predictive values of the ANN and SVM models are 986.180 kg·m-2 and 991.050 kg·m-2 , respectively.

키워드

과제정보

이 연구는 2022년도 한국연구재단 연구비 지원에 의한 결과의 일부임. 과제번호:NRF-2019R1A2C1088446

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