Abdul Gbadamosi Abdul.Gbadamosi@uwe.ac.uk
Research Associate - Big Data Application Development
Offsite construction: Developing a BIM-Based optimizer for assembly
Gbadamosi, Abdul Quayyum; Mahamadu, Abdul Majeed; Oyedele, Lukumon O.; Akinade, Olugbenga O.; Manu, Patrick; Mahdjoubi, Lamine; Aigbavboa, Clinton
Authors
Abdul Mahamadu Abdul.Mahamadu@uwe.ac.uk
Associate Lecturer - CATE - AAE - UAAE0001
Lukumon Oyedele L.Oyedele@uwe.ac.uk
Professor in Enterprise & Project Management
Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence
Patrick Manu
Lamine Mahdjoubi Lamine.Mahdjoubi@uwe.ac.uk
Professor in Info. & Communication & Tech.
Clinton Aigbavboa
Abstract
© 2019 Elsevier Ltd The lack of adequate consideration of the underlying factors affecting the methods of building assembly often results in inefficiencies in the uses of building materials, equipment and manpower. These inefficiencies are further compounded by the nature of the construction industry, which traditionally involves complex processes that result in wastages during production. To address this problem, this study integrates the principles of Design for Manufacture and Assembly (DFMA) and Lean Construction to develop a design assessment and optimization system to assist designers in the selection of alternative building design elements and materials in a building information model. This assessment and optimization system rely on metrics derived from production data associated with the ease of assembling, ease of handling, the speed of assembling and the wastage during assembly or construction of a building element or material. This paper presents the development of BIM-OfA assessment logic and its application for assessment and optimal selection of building envelop through the extension of Building Information Modelling (BIM). The system demonstrates its adequacy as an indicator of construction and material efficiency, its integration with BIM further enhances the practicality of using production data such weight of components, number of on-site workers and number of parts, for buildability assessment to improve efficiency and reduce waste.
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 11, 2019 |
Online Publication Date | Jan 14, 2019 |
Publication Date | Apr 1, 2019 |
Deposit Date | Feb 7, 2019 |
Publicly Available Date | Jan 15, 2020 |
Journal | Journal of Cleaner Production |
Print ISSN | 0959-6526 |
Electronic ISSN | 1879-1786 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 215 |
Pages | 1180-1190 |
DOI | https://doi.org/10.1016/j.jclepro.2019.01.113 |
Keywords | assembly, efficiency, DFMA, lean construction, building |
Public URL | https://uwe-repository.worktribe.com/output/849262 |
Publisher URL | http://doi.org/10.1016/j.jclepro.2019.01.113 |
Additional Information | Additional Information : This is the author's accepted manuscript. The final published version is available here: http://doi.org/10.1016/j.jclepro.2019.01.113. |
Contract Date | Feb 7, 2019 |
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