Field Test and Numerical Assessment of Helical Piles for Accelerated Bridge Construction

There is an increasing demand for resilient and sustainable foundation solutions suitable for supporting critical structures such as bridges. Accelerated bridge construction (ABC) is playing increasingly important roles in modern transportation networks to reduce the impact of bridge construction on the traveling public and increase the safety of workers. In partnership with Advanced Foundations Inc., the proposed research aims to investigate the efficacy of helical pile foundations for ABC projects, particularly for low-volume roads.

Multi-hazard Risk & Resilience Assessment for Real Asset Decision-Support (expansion of asset archetypes)

The proposed project is an extension of a previous project “Multi-hazard Risk & Resilience Assessment for Real Asset Decision-Support” which aims to develop a methodology and software for performing rapid high-resolution multi-hazard risk assessment for asset portfolios by combining commercial/publicly available hazard models with high-resolution vulnerabilities derived from multi-physics simulations of different asset archetypes. The proposed extension greatly expands the target number of archetypes from 50-100 to 300.

Seismic force modification factors for a new composite panel as seismic force-resisting system

Rising material costs, lack of quality control, labour shortages, challenging climates and significant on-site waste continue to be the challenges encountered by the Canadian construction industry. The collaboration between the research team and NEXII aims to develop the much-needed technical know-how for the new generation building panel products to address these challenges. This research concentrates on novel sandwich wall panels for use as the seismic force-resisting system (SFRS) of buildings with different archetypes, sizes, and connection systems.

Active noise cancellation of various noisy environments, especially construction sites, using advanced deep learning.

There are many commercialized active noise cancellation (ANC) headphones and headsets that try to reduce unwanted background noise. You can simply listen to your favorite music or talking with your loved ones in crowed areas using them without any distractions. The overall working principle of them is to generate an inverse signal to unwanted noise to neutralize it inside the ear drum. These devices are specially produced for single users. But how about cancelling excessive noise in car passengers’ cabin or working places that loud machineries are working?

Digital twin for structural health monitoring using deep learning and UAVs

This research project is about introducing an automatic mechanism for bridge inspection. Using unmanned aerial vehicle which are equipped with high resolution camera for early damage detection will reduce repairing cost and increase service life of bridge. This project will utilize the drones available in market and reprogram them to fly on a predefined path using GPS sensor and marker detection techniques. The images captured by drone will be processed by artificial intelligence system to detect corrosion, cracks and loosen bolts in the bridge.

Surface and subsurface damage segmentation using thermography and deep learning

Infrastructure facilities in Canada and many other places worldwide continuously deteriorate. Most structures either reached or exceeded their design service life. Bridges, buildings, roads, and other facilities deteriorate over time. To ensure the safety of these structures, visual inspections are routinely carried out by trained engineers. However, these visual inspections have some critical disadvantages, such as risks to the inspector, and visual inspections are time-consuming and erroneous.

Sustainable solution to chronic housing needs in the Canadian North

As Canada's economy and population continue to rise, primarily due to substantial immigration, there is an urgent need for new infrastructure, in particular, in remote regions of Canada. This can be achieved through advanced technologies to improve productivity of the Canadian construction industry while reducing the environmental impact of construction. This project proposes the Self-Deployable and Reconfigurable Structures (SDRS) where a ready-to-install building unit capable of deploying itself without the need for on-site construction machinery or labour.

Collaborative Machine Learning: using multiple mineral deposits to improve decision making in mineral deposits

Drillhole samples and remote sensing surveys provide information on subsurface resources for mineral resource estimation, mine planning, and project evaluation. The collection of this data is expensive but necessary to evaluate mineral resources and build mine plans. Additional data collected reduces subsurface uncertainty but is expensive to collect. Machine learning algorithms are becoming common in subsurface modeling workflows and as tools to assist decision making, but these algorithms perform better with access to more training data.

Forecasting key electricity end-uses in the residential sector, including electric vehicles (EV) charging

The current push towards electrification in Canada as a decarbonization strategy could lead to unintended consequences for utility providers, especially when managing peak demand to ensure grid stability. To address this issue, utilities require urban-scale energy models to forecast demand patterns and analyze the impact of new technologies, such as Electric Vehicles (EV).

Mitigation of Fouling of Tertiary UF Membranes at Low Temperatures

Membranes that are used in wastewater treatment have been found to clog more rapidly at cold temperatures. This study will examine alternative operating strategies that will reduce clogging and thereby reduce the needs for extra energy and chemical consumption under these operating conditions.