TY - JOUR
T1 - Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model
AU - Lu, Yanjie
AU - Ge, Yisu
AU - Zhang, Guodao
AU - Abdulwahab, Abdulkareem
AU - Salameh, Anas A.
AU - Ali, H. Elhosiny
AU - Nguyen Le, Binh
N1 - Publisher Copyright:
© 2022
PY - 2023/3
Y1 - 2023/3
N2 - A significant portion of the solid waste filling landfills worldwide is debris from construction and demolition projects. Across the world, a significant portion of the solid waste filling landfills is made up of construction and demolition waste. Recycling construction waste may help cut down on the quantity of waste sent to landfills and the requirement for energy and other natural resources. To help with construction waste reduction, a management hierarchy that begins with rethink, reduce, redesign, refurbish, reuse, incineration, composting, recycle, and eventually disposal is likely to be effective. The objective of this research is to investigate the viability of the Analytic Hierarchy Process (AHP) as a data gathering instrument for the development of a solid waste management assessment tool, followed by an examination of an artificial neural network (ANN). Using a standardized questionnaire, all data was gathered from waste management practitioners in three industry sectors. The survey data was subsequently analyzed using ANN and later AHP. The suggested framework consisted of four components: (1) the development of different level structures for fluffy AHP, (2) the calculation of weights, (3) the collection of data, and (4) the making of decisions. An ANN feedforward with error back propagation (EBP) learning computation is coupled to identify the association between the items and the store execution. It was found that the combination of AHP and ANN has emerged as a key decision support tool for landfilling, incineration, and composting waste management strategies, taking into account the environmental profile and economic and social characteristics of each choice. Composting has the highest sustainable performance when a balanced weight distribution of criteria is assumed, especially if the environmental component is considered in comparison to the other criteria. However, if social and economic features are addressed, incineration or landfilling have more favorable characteristics, respectively.
AB - A significant portion of the solid waste filling landfills worldwide is debris from construction and demolition projects. Across the world, a significant portion of the solid waste filling landfills is made up of construction and demolition waste. Recycling construction waste may help cut down on the quantity of waste sent to landfills and the requirement for energy and other natural resources. To help with construction waste reduction, a management hierarchy that begins with rethink, reduce, redesign, refurbish, reuse, incineration, composting, recycle, and eventually disposal is likely to be effective. The objective of this research is to investigate the viability of the Analytic Hierarchy Process (AHP) as a data gathering instrument for the development of a solid waste management assessment tool, followed by an examination of an artificial neural network (ANN). Using a standardized questionnaire, all data was gathered from waste management practitioners in three industry sectors. The survey data was subsequently analyzed using ANN and later AHP. The suggested framework consisted of four components: (1) the development of different level structures for fluffy AHP, (2) the calculation of weights, (3) the collection of data, and (4) the making of decisions. An ANN feedforward with error back propagation (EBP) learning computation is coupled to identify the association between the items and the store execution. It was found that the combination of AHP and ANN has emerged as a key decision support tool for landfilling, incineration, and composting waste management strategies, taking into account the environmental profile and economic and social characteristics of each choice. Composting has the highest sustainable performance when a balanced weight distribution of criteria is assumed, especially if the environmental component is considered in comparison to the other criteria. However, if social and economic features are addressed, incineration or landfilling have more favorable characteristics, respectively.
KW - Analytic Hierarchy Process (AHP)
KW - Artificial Neural Network (ANN)
KW - Energy saving
KW - Solid waste
KW - Sustainable green building
KW - Waste management
UR - http://www.scopus.com/inward/record.url?scp=85147315701&partnerID=8YFLogxK
U2 - 10.1016/j.chemosphere.2022.137708
DO - 10.1016/j.chemosphere.2022.137708
M3 - Article
C2 - 36621688
AN - SCOPUS:85147315701
SN - 0045-6535
VL - 318
JO - Chemosphere
JF - Chemosphere
M1 - 137708
ER -