Cha, G.-W.; Park, C.-W.; Kim, Y.-C.; Moon, H.J. Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks. Sustainability2023, 15, 16245.
Cha, G.-W.; Park, C.-W.; Kim, Y.-C.; Moon, H.J. Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks. Sustainability 2023, 15, 16245.
Cha, G.-W.; Park, C.-W.; Kim, Y.-C.; Moon, H.J. Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks. Sustainability2023, 15, 16245.
Cha, G.-W.; Park, C.-W.; Kim, Y.-C.; Moon, H.J. Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks. Sustainability 2023, 15, 16245.
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, ver-ifying them as excellent models. Moreover, a Shapley additive explanations analysis revealed that DWG was most impacted by the floor area for all the DW types, with a positive correlation with DWG. Conversely, other factors showed either a positive or negative correlation with DWG de-pending on the DW type. The study findings will enable demolition companies and local gov-ernments in making informed decisions for efficient DW management and resource allocation by accurately predicting the generation of various types of DW.
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.
