This research aims to develop a fully automated, BIM-integrated, multi-robotic platform for sustainable steel construction manufacturing. The proposed system will integrate industrial robot arms, smart welding, real-time quality inspection, automated cutting and bending processes, and material optimization. Expected outcomes include comprehensive simulation tools and a cyber-physical platform to enhance multi-robot coordination, scalability, and knowledge transfer across the industry.

This research aims to improve the performance of manual production in modular construction through data-driven productivity improvement, predictive assessment of ergonomic risk, and incremental adoption of automation (i.e., semi-automation).

This project aims to develop an automated, unmanned system that identifies immediate and anticipated hazards and suggests appropriate controls based on real-time video footage collected from construction sites. The proposed system will significantly enhance overall site safety by complementing manual safety personnel in detecting and proactively controlling hazards.

The proposed study will generate manufacturing protocols for masonry units using combustion ash and similar agro-forestry and industrial waste. Using 3D printing to manufacture specific geometries that impart auxetic properties (rendering common geopolymer into metamaterials), this research promises low-carbon footprint in production and energy efficiency and durability during functional life of masonry units. This project is supported by recent findings by the PI-CoP that have produced formulations using nanomaterials that are highly favourable for 3-D printability.

This research aims to develop AI agents that operate on engineering and construction domains and partially or fully automate the planning process of industrial construction with the aim of achieving "self-healing" schedules that adapt to changing site conditions and constraints.

This research aims to automate the safety and standard documentation generation and interpretation process, pre-construction hazard assessments, and information management for material planning. Technologies employed include artificial intelligence, such as machine learning, data analytics, deep learning, and generative AI, as well as 3D scanning. This research is expected to improve work front safety, production efficiency, and material management, thereby mitigating risk and decreasing project delays, cost escalations, and management burdens.

This research aims to develop an integrated decision support tool with dynamic and real-time capacities tailored specifically for the construction industry. The focus will be on residential construction (wood) and steel construction processes, developing implementation-ready, automated, real-time monitoring and control tools for both on-site construction processes and off-site fabrication. This research is expected to improve productivity and safety, reduce construction time, and enable real-time communication between stakeholders.

This project aims to develop an all-encompassing open-source digital platform tailored for complex industrial construction projects. The platform will integrate core functionalities such as visualization, constructability analysis, field-run scope design, and extended reality, through open-source technologies, client-supplied BIM, graph algorithms, WebXR/AR visualization, and multi-objective optimization techniques. This research will help to streamline constructability analysis and field-run scope design, allowing for more efficient allocation of construction resources.

This project aims to refine the research roadmap developed by the CIC in 2019 by identifying research themes on which to focus in the long-term. This may mean confirming areas of study already prioritized by the existing roadmap, or introducing new themes based on current and emerging research trends. The roadmap will form the basis for later identifying specific projects in each research theme, which may be short-, medium-, or long-term projects.

This project aims to determine the impact of recent changes to the construction engineering and management (CEM) job market, such as technological advances (e.g. robotics, artificial intelligence, Internet of Things) and the pandemic. The outcome of this study will be implemented at the CEM group at the University of Alberta to update the curriculum and prepare the graduates for the future job market.