Proceedings of the 2020 DigitalFUTURES 2021
DOI: 10.1007/978-981-33-4400-6_6
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Recycling Construction Waste Material with the Use of AR

Abstract: This paper aims to present a methodology for reusing and recycling scrap timber from building sites using augmented reality and flexible digital models. The project we present describes a process that enables existing material to be reused and repurposed such that the designed model is updated by the digital inventory of digitised offcuts/waste elements.

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Cited by 4 publications
(2 citation statements)
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“…Instead of the more traditional method of designing and documenting, the designer had a more flexible relationship with the design and the digitised inventory of parts. This technique reflects a fundamental shift in the design paradigm, where designers work with a blank slate of materials where cost or structural competence is the only constraining factor (Parry and Guy 2021).…”
Section: Closementioning
confidence: 99%
“…Instead of the more traditional method of designing and documenting, the designer had a more flexible relationship with the design and the digitised inventory of parts. This technique reflects a fundamental shift in the design paradigm, where designers work with a blank slate of materials where cost or structural competence is the only constraining factor (Parry and Guy 2021).…”
Section: Closementioning
confidence: 99%
“…Three potential aspects related to designing out waste are highlighted in previous studies related to AR: 1) planning materials reuse and recovery, 2) off-site construction, and 3) optimisation of material use. Parry and Guy (2020) attempted to demonstrate how with help of mixed reality fabrication technology, it can promote effective re-use of scrap timber pieces. Scrap timbers were categorised by their cross-section profiles, an interface to show wireframe holograms were used to mark timber pieces was developed in relation to the developed parametric model of a small non-structural object.…”
Section: Ar and Designing Out Wastementioning
confidence: 99%