Integrating construction supply chains within a circular economy: An ANFIS-based waste analytics system (A-WAS)

Akinade, Olugbenga O.; Oyedele, Lukumon O.

Abstract

© 2019 The circular economy agenda makes it paramount for construction supply chains to reduce material waste. Although a collaborative platform called Building Information Modelling (BIM) offers a means of supply chains integration, it has not been efficiently upscaled for delivering waste efficient building designs. This study, therefore, develops a BIM-based computational tool for building waste analytics and reporting in the construction supply chains. A Construction Waste (CW) prediction model using Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed and integrated into Autodesk Revit BIM platform. The model development process reveals that “Gross Floor Area” and “Construction type” are the two key predictors for CW. The results of the study show that the tool offers useful insights into CW minimisation opportunities. The study makes a huge contribution to CW management practices by developing a computational approach to CW measurement. The contribution of the study is fundamental because achieving accurate waste prediction is crucial to waste prevention through adequate design principles and BIM.

Citation

Akinade, O. O., & Oyedele, L. O. (2019). Integrating construction supply chains within a circular economy: An ANFIS-based waste analytics system (A-WAS). Journal of Cleaner Production, 229, 863-873. https://doi.org/10.1016/j.jclepro.2019.04.232

• Journal Article Type: Article
• Acceptance Date: Apr 19, 2019
• Online Publication Date: Apr 20, 2019
• Publication Date: Aug 20, 2019
• Deposit Date: Apr 30, 2019
• Publicly Available Date: Apr 21, 2020
• Journal: Journal of Cleaner Production
• Print ISSN: 0959-6526
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 229
• Pages: 863-873
• DOI: https://doi.org/10.1016/j.jclepro.2019.04.232
• Keywords: construction supply chains, circular economy, construction waste analytics, building information modelling (BIM), predictive modelling
• Public URL: https://uwe-repository.worktribe.com/output/848368
• Publisher URL: http://doi.org/10.1016/j.jclepro.2019.04.232
• Additional Information: Additional Information : This is the author's accepted manuscript. The final published version is available here: http://doi.org/10.1016/j.jclepro.2019.04.232.