Formerly titled "AI-powered Generative Design and Manufacturing for Prefabricated Buildings."

The construction industry is an important sector in Canada, employing over 1.4 million people and generating about $141 billion to the economy annually, which accounts for 7.5% of Canada’s gross domestic product (GDP). Building construction is a major component of the construction sector, considering its energy consumption and greenhouse gas (GHG) emissions. Over the last decades, the building construction sector has remained a labour-intensive industry with low productivity compared with other sectors. As we confront challenges such as labour shortages, increased energy efficiency standards, and rising housing requests, there is an increasing demand for more efficient and cost-effective design and construction methods. The introduction of building prefabrications has brought value in solving these issues. They can provide a more environmentally friendly construction process with higher efficiency and quality, and at a reduced cost. However, there still exist challenges in the design and manufacturing of prefabricated buildings. First, the designers and engineers are overwhelmed by making important decisions at the early stages of the design because changes in a later stage are difficult for prefabricated buildings. Second, the complexity of design is higher, which limits the variety of design options, because the manufacturing process has to be considered at the design stage. Third, the manufacturing of prefabricated buildings still requires much human intervention, and productivity can be further improved. The research program aims to bring the latest Artificial Intelligence and Robotics technology into the design and manufacturing of prefabricated buildings, in order to further boost productivity and sustainability. The research will mainly be focusing on the design and manufacturing of two building systems, i.e., light wood-frame and mass timber, but the technologies and concepts developed in this program can be extended to other building systems.

Buildings consume 1/3 of global primary energy, contribute 40% of greenhouse gas (GHG) emissions globally, and people spend more than 85% of their time in buildings. Despite the development of modern technologies, the building industry lags behind in terms of productivity and energy efficiency. Mass timber construction is a novel alternative solution to traditional light-frame wood construction. It advocates for an engineered solution to substitute concrete and steel for wood, a more sustainable material. However, mass timber construction still struggles with a wide variety of problems: inflexible manufacturing operations, unsatisfactory project performance, and low productivity. For the past years, research has shown that digitalization can solve most of these pitfalls in the construction industry. Indeed, the digitalization of the mass timber industry would increase the profitability of existing business models and investments while supporting a more sustainable construction process. Digital technologies, such as building information models (BIM) or digital twins (DT), are proven to be the key to achieve important improvements in operational performance. The integration of both technologies provides a clear link between the product design and the manufacturing process, enabling bi-directional information channels between both and determining relationships between design parameters and operational performance. This project aims to develop novel digital technologies for mass timber operations in a comprehensive manner, focusing on three areas: productivity, safety, and waste generation, while supported by advanced manufacturing methods, such as robotics or lean manufacturing.

Formerly titled "Data-Driven Decision-Making Methods for Improved Design, Fabrication, Construction and Deconstruction of Steel Structures."

The main objective of this project is to develop data-driven methods for decision-making support in order to enhance safety, quality, cost-efficiency, sustainability, and productivity in the steel construction industry. The specific objectives of this project are to: (1) Understand the steel design, fabrication, erection and repair workflows; (2) Develop simulation models of steel design, fabrication, erection, repair, demolition and deconstruction in collaboration with industry professionals through data collection from the industry and supplementary models ; (3) Develop automated decision-making methods for design, fabrication, erection, repair, demolition and deconstruction of structural steel; and (4) Propose recommendation for implementing the developed methods into steel industry workflows (design engineers, project managers and operations personnel) to allow better collaboration and data exchange among project stakeholders.

This project aims to develop a technology to minimize the spread of biological hazards and eliminate the risk of infection to pathogenic micro-organisms in ventilated buildings. In normal conditions, alarge portion of the supply air to rooms consists of the recirculated air to conserve energy. Hence, the ventilation system can transport indoor air contaminants such as particulates, airborne microbial agents, and organic toxins, posing safety and health hazards to the occupants. Also, filters can entrap and accumulate micro-organisms. They may even become a breeding ground for fungi and bacteria, turning the filter into a biologically hazardous object. Any change or disturbance in the airflow may release some of the microbes into the environment. The filter disposal may also become hazardous unless a safe disposal protocol is followed to minimize the risks. As such, it is essential to disinfect the filters to inactivate the microbes, avoid creating a medium for multiplication of the pathogens and prevent the risk of episodic release of the germs into the building. This is particularly important during pandemics and in critical places such as hospitals and clinics where the air’s pathogen concentration could be high. Although several technologies have been developed for air purification, these technologies are either ineffective or are costly or both. The goal of this project is to develop an effective and inexpensive air purification technology for broad applications in different settings.

This research project aims to evaluate the impact of exoskeletons on performance and safety associated with construction tasks. Specifically, the suitability of exoskeletons for physically demanding construction tasks will be assessed to understand the impact of wearing an exoskeleton from a kinematic, biomechanical, and usability perspective. The findings will also guide future research on design and development of customized exoskeletons specifically designed for construction tasks.

Formerly "Modular Wall Systems for Accelerated, Safe, and Sustainable Construction."

This project aims to revolutionize the design and construction of future buildings through an integrated interdisciplinary approach – to create innovative building blocks that are combined with multiple building functions (structural and thermal) and are suitable for modular and autonomous construction. These innovative blocks can form walls in any shape (curved, inclined, and tall) with desired openings to accommodate other building components (wood and steel structures, ventilation and electrical systems). These blocks can be assembled quickly by autonomous machines on or off sites. Therefore, the introduction of the new building blocks will enable very modular design and construction and fully utilize the power of artificial intelligence, computational design, BIM technologies, additive manufacturing, and robotic construction. The modular and multi-functional features of the blocks will significantly enhance the productivity of the design construction, and the accuracy of cost estimation and planning. Using autonomous construction with the blocks on or off site will minimize health and safety risks. The high thermal performance of the wall systems, low embodied energy production of the blocks, and the outstanding durability of masonry materials will minimize the overall construction environmental footprint.

Studies show that the residential sector is responsible for about 18% of Canada's energy consumption, nearly 60% of which is attributed to space heating. The space heating energy consumption of homes places heavy burdens on users in terms of energy costs, on the energy infrastructure in terms of high peak demand, and on the environment in terms of GHG emissions due to the burning of fossil fuel. These matters have led to a focus on improving the energy efficiency of Canadian dwellings. Improving the thermal performance of masonry walls can help reduce the energy consumption of buildings. To achieve this goal, designers require explicit guidelines and simple methods to predict their effective thermal resistance (R-value) with different configurations. Currently, the options to calculate the R-values of masonry walls consist of overly simplified assumptions that often lead to inaccurate results or expensive and time-consuming numerical modelling. The proposed project focuses on developing physics-based and artificial-intelligence-based models to estimate the overall thermal resistance of different masonry wall systems. The ultimate goal of the project is to develop innovative tools and provide intuitive guidelines that are universal, easy to use, and cost-free for the building industry. These outputs will increase productivity in the design phase, lower the cost of masonry wall construction, and enhance the business competency of the masonry industry. The project outcomes consist of economic diversification and technology development in the building construction sector. From the environmental perspective, these methods and tools will help improve the energy efficiency of buildings.

This project aims to develop an integrated, data-driven framework for safety planning and management to improve the decision-making associated with safety management systems and help mitigate potential safety hazards in different phases of the project life to proactively avoid the occurrence of safety incidents.