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

Akinade, Olugbenga O.; Oyedele, Lukumon O.

Authors

Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence



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
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.

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