Digitization and Data Analytics in Off-site Construction

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As an alternative approach to traditional stick-built construction methods, the off-site construction approach allows construction to be performed within a controlled environment, with components prefabricated and assembled into modules and units and then transported to construction sites for installation. However, as the traditional approach is replaced by off-site construction, construction data has become scattered throughout the supply chain, resulting in complex analytics. At this juncture, a new cognitive paradigm is required to understand the implementation of emerging data analytics techniques for offsite construction. In the manufacturing industry, rapid advancement of Industry 4.0 technologies, including cloud computing and the Internet of Things (IoT), has enabled in-depth data analysis through real-time data exchange. Machine learning and artificial intelligence (AI) techniques make it possible for autonomous decision-making such as proactive machine control and predictive maintenance. These advancements have disrupted manufacturing planning systems and human-machine interaction mechanisms. The digitization of the physical environment is often referred to as digital twinning, where seamless data exchange and decision-making protocols function simultaneously with industrial processes to create integrated cyber-physical systems. Recognizing the impact of digitization in the manufacturing industry, this research program will scientifically develop a framework to digitize the offsite construction supply chain and provide intelligent decision support tools for the construction industry. I plan to develop digital models (digital twins) for manufacturing facilities, transportation and delivery networks, and on-site construction assembly processes. Various decision-making protocols (namely predictive, simulation, and optimization models) will be tested and implemented in various applications. Through an immersive reality environment, I will explore how data-driven decisions can be translated to intuitive presentations to end-users in order to validate and improve decision support systems. The development of this research program will have a wide-reaching impact on construction project stakeholders by providing timely and accurate decision-making mechanisms. This will help boost construction productivity and, consequently, increase profitability of the Canadian construction industry. This research program will provide industry-engaging and cross-disciplinary training opportunities for Highly Qualified Personnel (HQP) at graduate and undergraduate levels. I will encourage and strive for Equity, Diversity and Inclusion (EDI) in my research program. The HQP will work closely with construction industry partners, collaborate with other research institutions/departments, and be exposed to interdisciplinary research topics and training.