Abstract
This study identifies evaluation criteria with the goal of appraising the performance of existing construction waste management tools and employing the results in the development of a holistic building information modelling (BIM) framework for construction waste management. Based on the literature, this paper identifies 32 construction waste management tools in five categories: (a) waste management plan templates and guides, (b) waste data collection and audit tools (c) waste quantification models, (d) waste prediction tools, and (e) geographic information system (GIS)-enabled waste tools. After reviewing these tools and conducting four focus-group interviews (FGIs), the findings revealed six categories of evaluation criteria (a) waste prediction; (b) waste data; (c) commercial and procurement; (d) BIM; (e) design; and (f) technological. The performance of the tools is assessed using the evaluation criteria and the result reveals that the existing tools are not robust enough to tackle construction waste management at the design stage. The paper therefore discusses the development of a holistic BIM framework with six layers: application; service domain; BIM business domain; presentation; data; and infrastructure. The BIM framework provides a holistic approach and organizes relevant knowledge required to tackle construction waste effectively at the design stage using an architecture-based layered approach. This framework will be of interest to software developers and BIM practitioners who seek to extend the functionalities of existing BIM software for construction waste management.
Original language | English |
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Pages (from-to) | 3-21 |
Number of pages | 19 |
Journal | International Journal of Sustainable Building Technology and Urban Development |
Volume | 7 |
Issue number | 1 |
Early online date | 23 Mar 2016 |
DOIs | |
Publication status | E-pub ahead of print - 23 Mar 2016 |
Keywords
- building information modelling
- Construction waste management tools
- evaluation criteria
- framework
- thematic analysis
- waste data
- waste prediction