Dr Lukman Akanbi Lukman.Akanbi@uwe.ac.uk
Associate Professor - Big Data Application Developer
Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy
Akanbi, Lukman A.; Oyedele, Lukumon O.; Omoteso, Kamil; Bilal, Muhammad; Akinade, Olugbenga O.; Ajayi, Anuoluwapo O.; Davila Delgado, Juan Manuel; Owolabi, Hakeem A.
Authors
Lukumon Oyedele L.Oyedele@uwe.ac.uk
Professor in Enterprise & Project Management
Kamil Omoteso
Muhammad Bilal Muhammad.Bilal@uwe.ac.uk
Associate Professor - Big Data Application
Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence
Anuoluwapo Ajayi Anuoluwapo.Ajayi@uwe.ac.uk
Associate Professor - Big Data Application
Manuel Davila Delgado Manuel.Daviladelgado@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence
Hakeem Owolabi Hakeem.Owolabi@uwe.ac.uk
Associate Professor - Project Analytics and Digital Enterprise
Abstract
© 2019 Despite the relevance of building information modelling for simulating building performance at various life cycle stages, Its use for assessing the end-of-life impacts is not a common practice. Even though the global sustainability and circular economy agendas require that buildings must have minimal impact on the environment across the entire lifecycle. In this study therefore, a disassembly and deconstruction analytics system is developed to provide buildings’ end-of-life performance assessment from the design stage. The system architecture builds on the existing building information modelling capabilities in managing building design and construction process. The architecture is made up of four different layers namely (i) Data storage layer, (ii) Semantic layer, (iii) Analytics and functional models layer and (iv) Application layer. The four layers are logically connected to function as a single system. Three key functionalities of the disassembly and deconstruction analytics system namely (i) Building Whole Life Performance Analytics (ii) Building Element Deconstruction Analytics and (iii) Design for Deconstruction Advisor are implemented as plug-in in Revit 2017. Three scenarios of a case study building design were used to test and evaluate the performance of the system. The results show that building information modelling software capabilities can be extended to provide a platform for assessing the performance of building designs in respect of the circular economy principle of keeping the embodied energy of materials perpetually in an economy. The disassembly and deconstruction analytics system would ensure that buildings are designed with design for disassembly and deconstruction principles that guarantee efficient materials recovery in mind. The disassembly and deconstruction analytics tool could also serve as a decision support platform that government and planners can use to evaluate the level of compliance of building designs to circular economy and sustainability requirements.
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 14, 2019 |
Online Publication Date | Mar 15, 2019 |
Publication Date | Jun 20, 2019 |
Deposit Date | Mar 21, 2019 |
Publicly Available Date | Jan 17, 2020 |
Journal | Journal of Cleaner Production |
Print ISSN | 0959-6526 |
Electronic ISSN | 1879-1786 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 223 |
Pages | 386-396 |
DOI | https://doi.org/10.1016/j.jclepro.2019.03.172 |
Keywords | Disassembly and Deconstruction Analytics (D-DAS), Building Information Modelling (BIM), end-of-life, circular economy, design for deconstruction, design for disassembly |
Public URL | https://uwe-repository.worktribe.com/output/846100 |
Publisher URL | https://doi.org/10.1016/j.jclepro.2019.03.172 |
Additional Information | Additional Information : This is the author's accepted manuscript. the final published version is available here: https://doi.org/10.1016/j.jclepro.2019.03.172. |
Contract Date | Mar 21, 2019 |
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Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy
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Copyright Statement
© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
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