Big data for design options repository: Towards a DFMA approach for offsite construction

Gbadamosi, Abdul-Quayyum; Oyedele, Lukumon; Mahamadu, Abdul-Majeed; Kusimo, Habeeb; Bilal, Muhammad; Davila Delgado, Juan Manuel; Muhammed-Yakubu, Naimah

Abstract

A persistent barrier to the adoption of offsite construction is the lack of information for assessing prefabrication alternatives and the choices of suppliers. This study integrates three aspects of offsite construction, including BIM, DFMA and big data, to propose a Big data Design Options Repository (BIG-DOR). The proposed BIG-DOR system will connect BIM clients to manufacturers/supplier’s information such as prefab component cost and production lead times. In this study, we propose a framework for integrating BIG-DOR into the process of offsite construction delivery. The design of the BIG-DOR system architecture, as well as the key components such as the DFMA option-based 3D objects classifier, is presented. The contribution to the knowledge of this study is the successful integration of BIM, big data, DFMA and offsite construction in a single framework and the development of a design alternatives assessment system for offsite construction adoption using this framework.

• Journal Article Type: Article
• Acceptance Date: Aug 29, 2020
• Online Publication Date: Oct 3, 2020
• Publication Date: Dec 1, 2020
• Deposit Date: Sep 6, 2020
• Publicly Available Date: Oct 4, 2021
• Journal: Automation in Construction
• Print ISSN: 0926-5805
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 120
• Article Number: 103388
• DOI: https://doi.org/10.1016/j.autcon.2020.103388
• Keywords: Offsite construction, big data, DFMA, BIM, Big-DOR
• Public URL: https://uwe-repository.worktribe.com/output/6663743
• Publisher URL: https://www.journals.elsevier.com/automation-in-construction