Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model

Yanjie Lu, Yisu Ge, Guodao Zhang, Abdulkareem Abdulwahab, Anas A. Salameh, H. Elhosiny Ali, Binh Nguyen Le

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

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.

Original languageEnglish
Article number137708
JournalChemosphere
Volume318
DOIs
StatePublished - Mar 2023

Keywords

  • Analytic Hierarchy Process (AHP)
  • Artificial Neural Network (ANN)
  • Energy saving
  • Solid waste
  • Sustainable green building
  • Waste management

Fingerprint

Dive into the research topics of 'Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model'. Together they form a unique fingerprint.

Cite this