This project intends to make an existing Autonomous Truck Mounted Attenuator (ATMA) system fully operational for harsh Canadian weather conditions and develop an augmented perception framework to enhance motion planning of the control system. The primarily focus of an ATMA is ensuring the safety of highway workers and transportation infrastructure in work zones. Upon successfully gaining an understanding of the existing control system, the perception module will be augmented by Visual-LiDAR fusion and semantics integration into the ATMA system, through which reliable decision will make it possible to design a robust control system to address low visibility and perceptually degraded conditions (by Camera or LiDAR) in harsh weather scenarios. By utilizing a wide range of proprioceptive and exteroceptive sensory information, this project will enhanced safe navigation will be enhanced for motion planning and (vehicle) stabilization for the ATMA system.

To achieve the main objective of the project, two main tasks have been identified for this research project: facilitating enhanced perception and full control system integration for ATMA’s autonomous driving (including developing an enhanced detection system for perceptually degraded conditions using multimodal Visual-LiDAR fusion); and comprehensive testing in adverse Canadian weather conditions, with robustness enhancement of the ATMA’s longitudinal controls.

Building construction is an important industrial sector, providing employment opportunities, contributing markedly to Canada’s GDP, and addressing the demand for housing and institutional facilities. Yet the construction industry is lagging behind in terms of innovation, employing conventional construction methods associated with high rates of material waste, human error, rework, and occupational health hazards. To address these challenges and move the industry toward offsite construction, the NSERC Industrial Research Chair (IRC) in the Industrialization of Building Construction was established in 2011 as a joint initiative of the University of Alberta, the Natural Sciences and Engineering Research Council of Canada (NSERC), Alberta Innovates, and a consortium of industry partners from across the offsite construction supply chain. The IRC was successful in deploying novel research to facilitate the paradigmatic shift from conventional on-site construction to offsite construction methods. The next chapter will be to bring about a meaningful progression toward true manufacturing in building construction.

The objectives of the proposed research initiative are to characterize the current practice and use lean concepts to improve the productivity of the offsite building construction sector; develop technologies to facilitate mechanization and automation of the offsite building construction sector; develop a framework for data-driven planning and management in construction manufacturing; propose a building information modelling (BIM)-based framework to automate drafting and design for manufacturing, automate the assessment of GHG, and automate the assessment of building system energy demand to support low-energy building technologies; apply artificial intelligence (AI)-based tools to digitalize the manufacturing process; improve occupational safety through ergonomics studies; and develop a framework for onsite installation of prefabricated building components using mobile cranes.

The proposed research aims to build upon the expertise of the research team and novel discoveries in data analytics, fuzzy systems, lean construction, and simulation science to advance data-driven decision-making in construction. This research centers around the development of a smart object model that will act as a “data switchboard,” retrieving data from an organization’s multiple storage sites and distributing this information to various decision-support tools, as required. This approach was designed to maintain the integrity and utility of underlying systems, thereby respecting the desires of organizations to maintain and rely on existing ERP systems, BIM-driven tools, and in-house database solutions.

Moreover, the object model will be designed in a standardized format, allowing organizations to “plug-in” a variety of existing, commercially-available, and/or newly-developed decision-support tools without needing to customize each tool to suit specific data structures. Discoveries will be packaged and deployed as a decision-support tool-kit that will enable the safe, reliable, and timely mining of meaningful project information for the construction industry

The aim of this research is to enable the seamless integration of original project plans with as-built project information in order to improve construction planning and control. This includes enhancing both the collection of as-built project data, as well as the synchronization of that data with project plans. The research targets several management areas including (1) productivity monitoring and control, (2) risk assessment and forecasting, and (3) sustainability and waste reduction. Several research activities aimed at these three areas will run in parallel, each focusing on data collection and the development of deliverable standalone solutions to specific planning and control challenges. These solutions will later be integrated into a larger multi-dimensional framework to improve the forecasting and control of current builds and the planning of future projects.

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.

The main objective of this project is to develop a plug-and-play, modular, and intelligent energy system based on renewable resources and energy storage integration. Built on the work done in the previous project, this project will focus on product design, system level integration and field testing of the developed modules to increase their technology readiness level. The main objectives of this project are discussed as follows: (1) Mechanical/Thermal design of the enclosures; (2) System level integration; (3) Standard compliancy; (4) Energy management system development; (5) Real world testing and demonstration.

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.

The objectives of the proposed research initiative can be summarized as follows: (1) develop made-in-Canada technologies to facilitate mechanization and automation in Canada’s building manufacturing sector; (2) increase the productivity of automated building manufacturing processes through improved scheduling and production control; (3) improve the productivity of manual operations in building manufacturing through plant layout optimization and work redesign; (4) improve the efficiency and accuracy of building manufacturing through the introduction of automated, manufacturing-centric BIM tools; (5) enhance the operational performance of light-wood and light-gauge steel framing and sheathing through the incorporation of robotic technologies; (6) reduce process waste and human error in building manufacturing by increasing the level of digitalization in internal communication mechanisms, and production control; and (7) improve the occupational safety of building manufacturing through ergonomic analysis applications.

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.