- Title
- The classification of construction waste material using a deep convolutional neural network
- Creator
- Davis, Peter; Aziz, Fayeem; Newaz, Mohammad Tanvi; Sher, Willy; Simon, Laura
- Relation
- Automation in Construction Vol. 122, Issue February 2021, no. 103481
- Publisher Link
- http://dx.doi.org/10.1016/j.autcon.2020.103481
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2021
- Description
- The management of Construction and Demolition Waste (C&DW) is complex and adds significantly to the overall life cycle cost of projects. On site waste sorting using technologies that automatically identify different materials has the potential to assist in classifying C&DW and reduce costs. The aim of this research was to design and describe a deep convolutional neural network (CNN) to identify 7 typical C&DW classifications (both single and mixed disposal) using digital images of waste deposited in a construction site bin (artefact). This approach emulated authentic construction site scenarios where on-site sorting is difficult. A novel design science methodology was used. The experiments delivered 94% accuracy, classifying both single and mixed C&DW. This accuracy is important on projects where on-site sorting is attempted, as in practice bin contamination escalates project costs and reduces C&DW diversion from landfill. To illustrate potential of the research the innovative artefact is incorporated within a hypothetical case study describing its use in a circular C&DW business model.
- Subject
- construction and demolition waste; case study; deep cnn; neural network; SDG 12; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1444745
- Identifier
- uon:42394
- Identifier
- ISSN:0926-5805
- Language
- eng
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