Analysis of critical features and evaluation of BIM software: towards a plug-in for construction waste minimization using big data

Bilal, Muhammad; Oyedele, Lukumon O.; Qadir, Junaid; Munir, Kamran; Akinade, Olugbenga O.; Ajayi, Saheed O.; Alaka, Hafiz A.; Owolabi, Hakeem A.

Abstract

© 2016 Taylor & Francis. The overall aim of this study is to investigate the potential of Building Information Modelling (BIM) for construction waste minimization. We evaluated the leading BIM design software products and concluded that none of them currently support construction waste minimization. This motivates the development of a plug-in for predicting and minimizing construction waste. After a rigorous literature review and conducting four focused group interviews (FGIs), 12 imperative BIM factors were identified that should be considered for predicting and designing out construction waste. These factors were categorized into four layers, namely the BIM core features layer, the BIM auxiliary features layer, the waste management criteria layer, and the application layer. Further, a process to carry out BIM-enabled building waste analysis (BWA) is proposed. We have also investigated the usage of big data technologies in the context of waste minimization. We highlight that big data technologies are inherently suitable for BIM due to their support of storing and processing large datasets. In particular, the use of graph-based representation, analysis, and visualization can be employed for advancing the state of the art in BIM technology for construction waste minimization.

• Journal Article Type: Article
• Acceptance Date: Nov 2, 2015
• Online Publication Date: Jan 21, 2016
• Publication Date: Oct 2, 2015
• Deposit Date: Mar 2, 2017
• Publicly Available Date: Mar 2, 2017
• Journal: International Journal of Sustainable Building Technology and Urban Development
• Print ISSN: 2093-761X
• Electronic ISSN: 2093-7628
• Publisher: Taylor & Francis
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Issue: 4
• Pages: 211-228
• DOI: https://doi.org/10.1080/2093761X.2015.1116415
• Keywords: BIM, construction waste prediction and minimization, design out waste, waste prevention, big data analytics, NoSQL
• Public URL: https://uwe-repository.worktribe.com/output/840917
• Publisher URL: http://dx.doi.org/10.1080/2093761X.2015.1116415
• Additional Information: Additional Information : This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Sustainable Building Technology and Urban Development on 21st January 2016, available online: http://www.tandfonline.com/10.1080/2093761X.2015.1116415.
• Contract Date: Mar 2, 2017