This project is a preliminary step in developing cost-effective and performance-targeted hybrid structural solutions for lateral load resisting systems (LLRSs) of multistory buildings to resist extreme loads. Focusing on masonry-based hybrid frame-wall dual (HFWD) system and hybrid frame-wall bracing (HFWB) system, the proposed project aims to improve our knowledge and understanding of these systems' structural behavior, and developing pertinent analysis models, performance evaluation tools, and rational design methods or guidelines, in both contexts of conventional prescriptive design and modern performance-based design.

This interdisciplinary research project aims to develop a robust, reliable, and adaptive tool capable of generating accurate, project-specific electricity load profile forecasts. This tool will support future revisions to electrical codes and technical standards related to building and community electrical load calculations, and will mitigate constraints imposed by the electrical infrastructure on the development of high-performance, energy-efficient communities, thereby accelerating the adoption and implementation of the highest feasible energy performance tiers of the national building and energy codes.

The proposed 5-year research program aims to establish a basis of personnel, knowledge, supply chain, and capital such that by the conclusion of the program, Canada is in a position to develop self-sustaining commercial operations of additive manufacturing in large-scale metallic components. The technology explored is wire-arc additive manufacturing (WAAM), a latest generation approach to manufacturing combining robotics, welding, and advanced software. The ultimate goal of this project is to master the WAAM technology and develop a basis of HQP and capital that will enable self-sustaining commercial operations of this technology in Canada.

This project aims to develop an advanced cognitive monitoring to directly address risks related to crane operators’ cognitive function during crane operations. Research activities include developing a wearable electroencephalogram (EEG)-based cognitive monitoring framework that continuously tracks the operators’ cognitive failure risk during ongoing equipment operations, and an equipment operation training system that personalizes training sessions according to trainees’ cognitive responses.

This research develops a framework for legal professionals in the construction industry to create contracts optimized for collaborative project delivery. Focusing on contract language, decision-making and interaction, team selection, and addressing deficiencies in contracts identified by collaborative contract practitioners in Canada, this project is expected to provide the framework for professionals to develop an actual contract document.

This project is the first phase of a long-term research project that aims to develop tools for estimating and reducing of embodied carbon in civil infrastructure systems. The overarching objectives are to develop a tool that systematically quantifies the environmental impact of constructing steel buildings, and to propose guidelines for selecting structural and non-structural components to minimize embodied carbon in typical commercial and residential buildings.

This project aims to make an exiting autonomous truck mounted attenuator (ATMA) system fully operational for harsh Canadian weather conditions, and to improve motion planning of the control system. This will be achieved by augmenting the perception module with visual-LiDAR fusion and semantics integration into the ATMA system, then designing a robust control system to address low visibility and perceptually degraded conditions in harsh weather scenarios.

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.