Current MEng Project Opportunities
Master of Engineering (MEng) students have the option of pursuing a project under the supervision of a CivMin faculty member. Full details can be found within the program requirements.
Below you will find current project opportunities. In addition to the projects listed below, students are encouraged to reach out to CivMin faculty members to see who may be available and interested in supervising a project.
Note that the project outlines below are preliminary - should a supervisor ultimately agree to supervise you, key details including milestones, grading distribution, etc. will need to be finalized before you are formally enrolled in the project.
Research Area
Building Science
Faculty Supervisor
Professor Seungjae Lee
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Seungjae Lee at sjae.lee@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
Retrofitting residential building stock is crucial to reducing greenhouse gas emissions and improving quality of life. However, the cost and time of home energy evaluation, an essential step before building retrofit, have been major barriers. To overcome these barriers, students develop an AI agent, using generative AI technologies and large language models, that helps homeowners collect necessary information and perform preliminary home energy evaluations. This project focuses on curating existing documents and materials on building energy evaluation in a form which the AI agent can easily process and retrieve. Through this project, students first collect and review relevant documents written in human language. Secondly, they create expected question-answer pairs between homeowners and the AI agent. Finally, they develop a data schema for data collection and home energy evaluation. Students will gain knowledge and skills in building energy evaluation (audit), state-of-the-art AI technologies, and data management.
Milestones
Milestone 1: Collect and review building energy evaluation documents (Months 1-3).
Milestone 2: Create expected question-answer pairs between homeowners and the AI agent (Months 4-5).
Milestone 3: Develop a data schema for data collection and home energy evaluation (Months 6-7).
Milestone 4: Produce a final report and present findings (Month 8).
Prerequisites
- CIV575 – Studies in Building Science or equivalent
- Python programming skills.
Grading Distribution
- Participation in project and research meetings (20%).
- Development of data-driven models (70%).
- Completion of academic reports (10%).
Research Area
Building Science
Faculty Supervisor
Professor Seungjae Lee
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Seungjae Lee at sjae.lee@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
In the era of AI and machine learning, the building industry seeks data-driven solutions for building integration, operation, and maintenance, i.e., smart building solutions, to reduce greenhouse gas emissions and improve indoor environmental quality. The first step towards smart buildings is to develop reliable and robust computational models for building systems. This project focuses on comparing various machine learning approaches in developing data-driven models of building energy systems. Through this project, students will develop (train) models of building energy systems using multiple machine-learning approaches and evaluate them focusing on their reliability, scalability, and robustness. Students will gain knowledge and skills in data management, data-driven modelling, model evaluation, and high-performance computing, as well as building system integration, operation, and maintenance.
Milestones
Milestone 1: Compile datasets for data-driven modelling and set up coding and modelling environments (Months 1-2).
Milestone 2: Develop (train) and evaluate data-driven models for different building systems (Months 3-7).
Milestone 3: Produce a final report and present findings (Month 8).
Prerequisites
- Python programming skills.
- Knowledge of data-driven modelling and machine learning.
Grading Distribution
- Participation in project and research meetings (20%).
- Development of data-driven models (70%).
- Completion of academic reports (10%).
Research Area
Building Science
Faculty Supervisor
Professor Marianne Touchie
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Marianne Touchie marianne.touchie@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
This project focuses on developing potential in-situ retrofit strategies for window wall systems which are commonly used in high-rise residential buildings. As designed, this enclosure type often leads to thermal discomfort, excessive energy use, condensation leading to mould growth and a lack of privacy, depending on the fenestration ratio. First, this project will involve conducing a literature review on window wall retrofit methods. Next, working with industry partner Engineering Link, feasible strategies will be developed further and applied to a case study building. Finally, energy and cost modelling will be completed to evaluate potential performance from the perspective of energy savings, increased indoor surface temperature (in winter) and how any changes to the fenestration ratio impact daylighting.
Students will benefit from exposure to energy modelling and interactions with industry partners to appreciate the complexity of enclosure design. The ultimate goal of this project is to develop retrofit approaches to improve the performance of these window wall systems, without having to replace the entire system thus avoiding significant embodied carbon impacts.
Milestones:
Milestone 1: Complete literature review on window wall retrofit strategies (Month 1-2)
Milestone 2: Analyze case study building and develop potential in-situ retrofit strategies (Month 3-4)
Milestone 3: Create pre and post-retrofit energy models to evaluate performance (Month 5-7)
Milestone 5: Produce a final report and present findings (Month 8)
Prerequisites
- CIV575 – Studies in Building Science
- CIV578 – Design of Building Enclosures
Grading Distribution
- Participation in project and research meetings (20%)
- Development of retrofit plan and energy model (30%)
- Completion of academic reports (50%)
Research Area
Building Science
Faculty Supervisor
Professor Sarah Haines
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Sarah Haines s.haines@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Fall 2024, Winter 2025
Course Weight
CIV1001 (One Half Credit Equivalent) or CIV1002 (Two Half Credit Equivalent)
Project Description
This project focuses on optimizing processes for fungal (mold) quantification on indoor dust and building material samples using novel digital polymerase chain reaction (dPCR) techniques. Quantitative PCR has become increasingly common for quantifying mold growth in indoor environments, however, with the emergence of next generation of PCR, dPCR, it is critical that we explore the feasibility of this technology to optimize throughput and efficiency of indoor fungal samples. Through this project students will conduct an initial review of literature as well as experimentation to optimize use of a Qiacuity Digital PCR machine for fungal quantification from different materials (dust, drywall, filters, etc). Students will gain skills in laboratory experimentation using novel dPCR equipment as well as exploring mold growth properties of common building materials learning about water activity and moisture content. The ultimate goal of this project is to develop a fully tailored pipeline for quantifying indoor fungal samples utilizing dPCR for high throughput sample analysis and quantification that may be used when classifying mold status of homes.
Milestones
Milestone 1: Complete literature review and background review on dPCR (Month 1).
Milestone 2: Increase laboratory skills (DNA extraction techniques, dPCR) (Month 2-3).
Milestone 3: Optimize dPCR protocol for fungal samples Analyze the dataset to assess performance metrics and feasibility (Month 4 - 6).
Milestone 4: Develop protocol based on analysis (Month 7).
Milestone 5: Produce a final report and present findings (Month 8).
Prerequisites
- Wet lab experience desired
- Python programming skills.
Grading Distribution
- Participation in project and research meetings (20%)
- Data collection and analysis (30%)
- Completion of academic reports (50%)
Research Area
Building Science
Faculty Supervisor
Professor Jeffrey Siegel
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Jeffrey Siegel (jeffrey.siegel@utoronto.ca).
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or CIV1002 (Two Half Credit Equivalent)
Project Description
The air flow and associated impacts that results from opening a window are often complex and beyond modeling capabilities. If used well, opening and closing windows at appropriate times can offset the need for mechanical heating and cooling, improve indoor air quality, and improve thermal comfort. The purpose of this project is to develop a training set of data and an AI model to provide insight to building occupants when to open or close the window. The project is intended to be scalable from the perspective that initially the model could be based on a short period of training data for a single parameter (e.g., temperature in one room) in one building. Later iterations could be based on weather or outdoor air quality predictions. Even more advanced iterations could be based on multiple inputs (e.g. window opening and shading operation) and multiple outputs (e.g. thermal comfort, indoor air quality, HVAC operation, etc.).
Milestones
Milestone 1: Deploy sensors and collect training data with scheduled window openings (Months 1-2.
Milestone 2: Develop AI model (Months 3-4).
Milestone 3: Deploy advisor and test (Months 5-6).
Milestone 4: Produce a final report and present findings (Months 7-8).
Prerequisites
- CIV375/575 – Building Science or equivalent
- CME538 – Introduction to Data Science or equivalent.
- Python programming skills.
Grading Distribution
- Participation in project and research meetings (20%)
- Data collection and analysis (30%)
- Completion of academic reports (50%)
Research Area
Concrete Materials
Faculty Supervisor
Professor Ibrahim Ogunsanya
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Ibrahim Ogunsanya Ibrahim.ogunsanya@utoronto.ca Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
Transportation ministries, bridge designers and engineers have shown increasing interest in using sacrificial/galvanic anodes for cathodic protection for the repair of reinforced concrete structures. For instance, sacrificial/galvanic anodes may be used in different bridge components, such as bridge deck overlays, substructure refacing, cantilever reconstruction and in concrete patch repairs. Decades of research have indicated that sacrificial/galvanic anodes can extend the service life of concrete patches, helping to reduce costly rehabilitation and traffic disruption. Although larger anodes in bridge deck overlays and substructure refacing have been mostly studied, the criteria for the successful performance of discrete anodes in concrete patches (in repaired locations) is a missing gap, mainly because of the difficulty in monitoring many different small-patched areas in structures. While the technology of sacrificial/galvanic anode has been around for decades, available products have evolved over time and a reliable performance metric to quantify them during use in service is needed.
Milestones
Milestone 1: Complete literature review on Galvanic Anodes in Concrete (Months 1-2).
Milestone 2: An experimental program designed to test the performance of at least 2-3 different anode types in reinforced concrete samples with varying degrees of chloride contamination, relative humidity and resistivity.
Milestone 3: Collect and analyze dataset to assess anode performance (Months 5-6).
Milestone 4: Develop anode performance metrics guideline and recommendations based on analysis (Month 7).
Milestone 5: Produce a final report and present findings (Month 8).
Prerequisites
- CIV201 – Civil Engineering Materials (or equivalent Materials-related course).
- Knowledge of Electrochemistry and corrosion.
- Knowledge of Python programming skills.
Grading Distribution
- Participation in project and research meetings (20%)
- Assistance with data collection and analysis (50%)
- Completion of academic reports (30%)
Research Area
Construction Management
Faculty Supervisor
Professor Tamer El-Diraby
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Tamer El-Diraby tamer.diraby@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or CIV1002 (Two Half Credit Equivalent)
Project Description
In one estimate, if we are to meet the 2050 net-zero targets, a facility must be rehabilitated every minute! While the technical aspects of doing so are complex, we have enough knowledge and tools to design and implement the decarbonization of our existing asset stock. The true challenges to scaling such rehabilitation are administrative/logistical and socio-economic ones.
Imagine if the operator of a small store, the owner of a single-family house or students in a high school want to assess means to decarbonize their facilities. They will face extensive hurdles to find and synthesize the web resources that can provide them with the right background knowledge of what to do: how to calculate the carbon footprint of their facility, what can be done to decarbonize their facilities?
In this research project, students, practitioners and community members will develop a sandbox (interactive online platform), where stakeholders (professionals or facility users) can study alternatives for decarbonization.
The immediate outcome of this project is an online sandbox where users can create a digital twin of their facilities and use AI to generate and study decarbonization scenarios. We plan to do this in a way that accommodates the needs of professional users and the general public. Neither should worry about technical analyses—thanks to AI, these will be served to them based on simple input. A user can upload a video or write a brief description of their facility to generate a BIM model of the physical aspects of their facility. Using a social BIM platform called Green 2.0 (previously developed at U of T), users will be able to comment on specific elements of the facility, including facility usage, user needs, and limitations on renovation scenarios. Much like Facebook, Green 2.0 uses semantic and social network analysis to capture the non-physical aspects of the facility: conditions, and user/functional profile. Using the knowledge graph (KG) technology and ChatGPT, users can get answers to their questions.
On the back end, a set of machine learning (ML) algorithms will use data provided by partner organizations to 1) capture decarbonization (renovation) options/best practices: what works, for who and how to do it; and 2) predict the costs of each option. A generative AI system will couple the BIM model and ML to generate alternative BIM models of possible renovation scenarios that match the user/facility profile.
Prerequisites
- None
Grading Distribution
- Participation in project and research meetings (10%)
- Assistance with data collection and analysis (40%)
- Completion of academic reports (50%)
Research Area
Construction Management
Faculty Supervisor
Professor Tamer El-Diraby
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Tamer El-Diraby tamer.diraby@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or CIV1002 (Two Half Credit Equivalent)
Project Description
A digital twin (DT) is not a digital/3D/flythrough/VR model of a building. It is also not limited to collecting building real-time data (from a Building Automation System—BAS). It is not a competitor to BAS or the new analytics tools that can predict hardware failure (HVAC or controls). DT is about optimal asset management of smart buildings: a data-driven, evolutionary optimization of business (not only technical) decision-making to achieve the highest performance? Performance here refers to 1) effectiveness: safety, functionality, and service quality; 2) efficiency: high ROI (optimal & timely investments), lower impact on climate, and the promotion of sustainability and resiliency. DT virtualizes the physical-cyber-social aspects of a building to understand the what: using data generated by the building to capture/evaluate current performance; analyze the impact of operational parameters on performance (the why); use machine learning to predict future conditions (what’s next); simulate the impacts of changes in policy and operations scenarios on performance (the how) in the virtual world before implementing them in the real world. Students working on this project will conduct business analysis work to develop specific use cases as to where and when digital twinning can be valuable to decision-making in the construction and the built asset industry. For example, DT provides managers with a data-rich BIM to visualize data and trends; and to use. analytics for generating business intelligence insights in the forms below: Descriptive models (what is going on): for example, assessment of decisions that contribute to energy waste; understanding factors that impact service levels or user satisfaction; modelling the impact of facility usage type or schedule on GHG emissions. Predictive models (what can/will happen): for example, expected cost of repair or maintenance of hardware (supplier-provided failure prediction models are to be used as input here); facility deterioration curves; and expected energy use. Prescriptive models (what if/what can be done): here, we virtualize (not visualize) complete future scenarios (changing business practices and technical schemes). For example, what is the combined benefits of changing facility usage, different occupancy levels, schedule and asset management policies on user satisfaction or facility performance (effectiveness and efficiency). Which climate action scenario is better? How to manage the next pandemic/emergency? A key feature of this project is the collaboration between the research team and the Facilities and Services Dept at U of T. This partnership ensures that the research not only addresses theoretical aspects but also practical, real-world applications.
Prerequisites
- None
Grading Distribution
- Participation in project and research meetings (10%)
- Assistance with data collection and analysis (40%)
- Completion of academic reports (50%)
Research Area
Construction Management
Faculty Supervisor
Professor Tamer El-Diraby
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Tamer El-Diraby tamer.diraby@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or CIV1002 (Two Half Credit Equivalent)
Project Description
A team from the Dept. of Civil & Mineral Engineering, U of T and NRC will conduct research on optimal LCA (Life Cycle Analysis) for mass timber structures. The project will include a comparative analysis of two actual buildings under construction at the University of Toronto: the new Academic Tower (a 14-story mass timber structure) and the Lawson Centre for Sustainability. These two buildings will be contrasted with recently built similar buildings at U of T.
Mass timber (MT) structures play a major role in the future of the built asset industry because of their potential for reducing carbon footprint. Actual implementation showed that these structures are highly appreciated by occupants due to their natural/warm look. Because of the novelty of this sector of the industry (most buildings are in the construction or early operations phases), we, however, have limited direct knowledge about their operations, including managing their post-construction LCA. Variability in the LCA results is a challenge to decision-makers.
A key feature of this project is the collaboration between the student and the asset managers at U of T. This partnership ensures that the research not only addresses theoretical aspects but also practical, real-world applications. Students will benefit from exposure to industry standards and practices.
This project will conduct a comprehensive review and analysis of the current variable wbLCA methodologies and tools. Findings will be examined on actual MT buildings—one is considered a tall MT building. Contrasting two MT buildings can provide insights into the relative importance of factors in LCA. The two buildings will be contrasted with another traditional building to further understand the true difference that MT buildings provide. Comparing the lifecycle costs of Tall MT buildings to conventional counterparts ultimately enables more informed decision-making about the choices of construction materials and methods.
Prerequisites
- None
Grading Distribution
- Participation in project and research meetings (10%)
- Assistance with data collection and analysis (40%)
- Completion of academic reports (50%)
Research Area
Construction Management
Faculty Supervisor
Professor Tamer El-Diraby
External Advisors
Experts from the Ministry of Transportation, Ontario (MTO)
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Tamer El-Diraby tamer.diraby@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or CIV1002 (Two Half Credit Equivalent)
Project Description
This project aims to help MTO re-develop the criteria by which funds are allocated to construction projects. With a limited budget and increasing needs for rehabilitating existing assets, how can projects be prioritized? A key issue that should be considered in the new criteria is the migration from focusing on prioritizing projects based on the level of deterioration of assets. Instead, the prioritization criteria must include consideration for the levels of services, and the environmental and social footprints of the projects. Another key issue is to expand the scope of analysis to include all assets in the Ontario highway infrastructure systems, such as culverts and other structures. Finally, a key consideration in the new criteria must be the availability of reliable data and objective methods to assess each criterion for each project.
The project will involve a comprehensive review of existing practices at related jurisdictions in Canada, the USA and internationally; synthesizing the conflicting criteria that are used by other departments of transportation (DOTs); situating the criteria to Ontario conditions; and assessing the feasibility of criteria usage in MTO context.
A key feature of this project is the collaboration between the university and MTO. This partnership ensures that the research not only addresses theoretical aspects but also practical, real-world applications. Students will benefit from exposure to industry standards and practices, enhancing their understanding of how projects are evaluated, how budgets are allocated and how decisions are communicated and justified to the public. The ultimate goal is to develop guidelines and recommendations for updating the decision-making process at MTO to match the goals of public policy and advance the well-being and return on investments of Ontarians.
Prerequisites
- None
Grading Distribution
- Participation in project and research meetings (10%)
- Assistance with data collection and analysis (40%)
- Completion of academic reports (50%)
Research Area
Construction Management
Faculty Supervisor
Professor Tamer El-Diraby
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Tamer El-Diraby tamer.diraby@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or CIV1002 (Two Half Credit Equivalent)
Project Description
Inefficiencies in the permitting process are increasingly becoming a key factor in construction delays and, hence, the exasperation of the housing affordability problem in Ontario (and Canada-wide). Automating parts of the process through the use of digitized construction practices can help enhance the efficiency of the process. In several other jurisdictions, Building Information Modeling (BIM) has been used to This project aims to help MTO re-develop the criteria by which funds are allocated to construction projects. With a limited budget and increasing needs for rehabilitating existing assets, how can projects be prioritized? A key issue that should be considered in the new criteria is the migration from focusing on prioritizing projects based on the level of deterioration of assets. Instead, the prioritization criteria must include consideration for the levels of services, the environmental and social footprints of the projects. Another key issue is to expand the scope of analysis to include all assets in the Ontario highway infrastructure systems, such as culverts and other structures. Finally, a key consideration in the new criteria must be the availability of reliable data and objective methods to assess each criterion for each project.
The project will involve a comprehensive review of existing practices at related jurisdictions in Canada, USA and internationally; synthesizing the conflicting criteria that are used by other departments of transportation (DOTs); situating the criteria to Ontario conditions; and assessing the feasibility of criteria usage in MTO context.
A key feature of this project is the collaboration between the university and MTO. This partnership ensures that the research not only addresses theoretical aspects but also practical, real-world applications. Students will benefit from exposure to industry standards and practices, enhancing their understanding of how projects are evaluated, how budgets are allocated and how decisions are communicated and justified to the public. The ultimate goal is to develop guidelines and recommendations for updating the decision-making process at MTO to match the goals of public policy and advance the wellbeing and return on investments for Ontarians.
Prerequisites
- None
Grading Distribution
- Participation in project and research meetings (10%)
- Assistance with data collection and analysis (40%)
- Completion of academic reports (50%)
Research Area
Environmental Engineering
Faculty Supervisor
Professor Mohammed Basheer
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. Mohammed Basheer mohammed.basheer@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
On July 16, 2024, Toronto experienced a flooding event that caused widespread disruption to public transportation and electricity supply for several hours. This project aims at modelling the hydrological impacts of this flooding event as well as future scenarios. We aim to evaluate engineering and nature-based solutions for flood risk mitigation and assess their effectiveness under various future climate projections. The project has the following objectives:
- Reviewing existing literature and models developed for the Greater Toronto Area.
- Developing and calibrating a detailed two-dimensional unsteady flow model incorporating rain-on-grid for the July 16, 2024, flood event.
- Evaluating the robustness of various flood risk mitigation measures, including both engineering solutions (e.g., levees and dams) and nature-based solutions (e.g., wetland restoration, green infrastructure) considering climate change.
Students participating in this project will have the opportunity to develop and enhance a range of highly sought-after skills, including geospatial analysis using GIS tools and computer programming, hydrodynamic modelling to simulate flood events and assessment of the effectiveness of flood risk mitigation measures under climate change. The findings of this research will provide valuable insights to policymakers and urban planners. By offering a robust model for flood risk evaluation, this project can help communities prepare for and mitigate the impacts of future flooding events.
Milestones
Milestone 1: Literature review of flood risk assessment studies and models of Toronto (Month 1).
Milestone 2: Collection and pre-processing of data needed for model development (Month 2).
Milestone 3: Development of two-dimensional hydrodynamic models (Month 3-5).
Milestone 4: Assessment of engineering and nature-based solutions (Month 6-7).
Milestone 5: Producing final report and results dissemination (Month 8).
Prerequisites
- CIV550 – Water Resources Engineering.
- Python programming skills.
Grading Distribution
- Participation in research meetings (20%)
- Development of a hydrodynamic model for the Don River (20%)
- Assessing flood risk management strategies (10%)
- Completion of academic reports (50%)
Research Area
Environmental Engineering
Faculty Supervisor
Professor David Meyer
External Advisors
Dr. Ben Tidwell, Senior Technical Advisor at World Vision (a charity that builds water systems)
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. David Meyer david.meyer@civmin.utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
This project focuses on designing more reliable rural, solar-powered water systems. To do so, we need to better understand the tradeoffs between system reliability, storage capacity, generation capacity. These factors have been explored in some depth to inform the design of solar-powered electricity grids, but these methods and insights have not yet been adapted to solar-powered water system (where storage is often a physical water tank rather than a battery). The project will build on existing Python scripts to process and learn from decades of historic, hourly data about solar insulation around the globe. From this historic data, we will consider daily, seasonal and annual variations in solar insolation, to determine how reliability varies with the relative sizes of solar generation capacity and storage. This project combines design and analysis practices used for renewable energy and water distribution systems.
This project features a close collaboration with experts at a large international charity that installs solar-powered water systems in rural areas of sub-Saharan Africa (World Vision). While the charity routinely installs solar-powered water systems, they are seeking our help to improve the sizing and reliability of these systems through new data-informed guidelines and processes. This project’s partnership with the implementing organization ensures that the research addresses both theoretical and practical aspects of real-world applications. Students involved in the project will benefit from exposure to a large international development organization and gain experience with standards and practices used for water system design and renewable energy systems. The ultimate goal of this project is to develop methods, guidelines, and recommendations for implementing reliable solar-powered water systems and balancing costs and reliability to improve water security in vulnerable regions.
Milestones
Milestone 1: Complete literature review on methods and data sources to predict reliability and daily-variability of photovoltaic power generation, with or without storage. Complete literature review on costs of solar-powered rural water systems, with a focus on the cost of storage. (Month 1-2).
Milestone 2: Combine the discovered methods and datasets for analyzing solar reliability to design the size of panels and storage for a water system in an example location in rural Zambia (Month 3-4).
Milestone 3: Automate your analysis to be applicable to new locations (Month 5)
Milestone 4: Apply your scripts and automated analysis to many new locations to unearth more generalized guidelines and recommendations for reliable rural water system design (Month 6-7).
Milestone 5: Produce a final report and present findings (Month 8).
Prerequisites
- CME538 – Introduction to Data Science or equivalent.
- Python programming skills.
Grading Distribution
- Participation in project and research meetings (20%)
- Assistance with data collection and analysis (30%)
- Completion of academic reports (50%)
Research Area
Environmental Engineering
Faculty Advisors
Professors Ron Hofmann and Yuri Lawryshyn
External Advisors
Keith Bircher, DeNora; Dr. Erin Mackey, Brown & Caldwell
Application
Please email your CV and a single paragraph describing your interest in the project to
Prof. Ron Hofmann ron.hofmann@utoronto.ca
Application Deadline
Rolling
Start Date
Fall 2024
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
Water treatment using ultraviolet light is becoming increasingly common, with the UV used to disinfect microorganisms, or in some instances used to destroy chemicals. There are several types of UV light sources. One such source is medium pressure mercury arc discharge lamps, which emit light at a range of wavelengths. Some of these wavelengths can convert nitrate, if present in the water through (for example) agricultural pollution, to nitrite, which is toxic and can also interfere with the overall treatment performance. While this conversion of nitrate to nitrite is theoretically possible, there is reason to believe that under normal operating conditions, it is unlikely to occur to a great enough extent to be a problem. But this has never been demonstrated.
This project will include a literature review and report on basic UV technology, followed by a focus on the specific photochemistry relevant to nitrate photolysis to nitrite. The second part of the project will involve using a computational fluid dynamic (CFD) model to predict nitrite formation under a range of treatment conditions. It is expected that a peer-reviewed journal article will be prepared from this work.
The project will require knowledge of CFD. To that end, the student will be required to take MIE504H “Applied Computational Fluid Dynamics” in the winter term of 2025 unless they have previously received similar training.
Milestones
Milestone 1: Complete literature review (Month 1-4).
Milestone 2: Learn CFD and modelling techniques (Month 5-8).
Milestone 3: Conduct CFD modelling (Month 9-10).
Milestone 4: Produce final report/paper (Month 11).
Prerequisites
- General environmental engineering background.
Grading Distribution
- Final report (100%)
Research Area
Environmental Engineering
Faculty Supervisor
Professor David Meyer
Application
Please submit your CV and a single paragraph describing your interest in the project in one file within a single email to Prof. David Meyer david.meyer@civmin.utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
Water scarcity can force water pipe networks to operate intermittently. Globally, 20% of piped water users, amounting to 1 billion people, receive water for only a few hours per day or week. Intermittent Water Supply (IWS) networks present unique hydraulic challenges that standard commercial or open-source software cannot adequately address. My research group has recently developed Python-based simulation tools to represent the hydraulics of water pipe networks that drain and fill daily. This project seeks to leverage and enhance these tools to better understand and improve the equity of IWS.
This project integrates into ongoing hydroinformatics and equity research, offering participating MEng students the opportunity to collaborate with a team of MASc and PhD students working on complementary topics. The project provides students with hands-on experience in augmenting existing hydraulic simulation packages, scripting and automating hydraulic simulations, and leveraging advanced data science techniques to improve water system design and operations. The skills developed will be valuable in various contexts, with the immediate application focused on improving the equity of water supply in low- and middle-income countries.
Milestones
Milestone 1: Complete literature review on hydraulic models of intermittent water supply and its (in)equity. Complete a complimentary review on optimization of water system operations (Month 1-2).
Milestone 2: Combine the reviewed methods to an intermittent water network model to uncover design and operational opportunities to improve equity (Month 3-4).
Milestone 3: Automate your analysis to be applicable to dozens of different network models (Month 5)
Milestone 4: Apply your automated analysis to many these models to unearth more generalized guidelines and recommendations for more equitable operations of intermittent water systems (Month 6-7).
Milestone 5: Produce a final report and present findings (Month 8).
Prerequisites
- CME538 – Introduction to Data Science or equivalent.
- Python programming skills.
- CIV1303 - Water Resources Systems Modeling, or equivalent (e.g. CIV340)
Grading Distribution
- Participation in project and research meetings (20%)
- Assistance with data collection and analysis (30%)
- Completion of academic reports (50%)
Research Area
Environmental Engineering
Faculty Advisors
Professors Ron Hofmann
Application
Please email your CV and a single paragraph describing your interest in the project to
Prof. Ron Hofmann ron.hofmann@utoronto.ca
Application Deadline
Rolling
Start Date
Fall 2024 or Winter 2025
Course Weight
CIV1001 (One Half Credit Equivalent)
Project Description
This is a single course equivalent MEng project to report on water quality requirements for fish farming (aquaculture) in a closed system, and on technologies widely applied to treat water to the required quality. The work is broadly related to the context of salmon farming in an urban environment in engineered aquariums.
Milestones
Milestone 1: Produce a final report (Months 1-4)
Prerequisites
- General environmental engineering background.
Grading Distribution
- Final report (100%)
Research Area
Structural Engineering
Faculty Supervisor
Professor Jeffrey Packer
Application
Interested MEng students should submit one email to Prof. Jeffrey Packer jeffrey.packer@utoronto.ca
with the following documents combined into one pdf file: CV, unofficial transcripts (at U of T and for Bachelor’s or other degrees), one paragraph describing their interest in the project, and one paragraph describing how they have the skills required.
Application Deadline
Rolling
Start Date
Fall 2024 or Winter 2025
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
Environmental concerns are a pressing issue for all new building projects, especially the embodied carbon content, with various construction materials vying for the leading position. The whole life of a structure (from “cradle-to-grave”) has been a popular time frame, but “cradle-to-cradle” measurement is now advocated. It is estimated that only 6% of construction materials in Canada are currently from recycled sources, which is a low level internationally. Thus, the “circular economy” for our built environment has become a paramount topic; for structural projects either entire buildings can be renovated for reuse or building materials can be reused. This project focuses on the latter, and specifically the reuse of structural steel members and components. A report will be produced which: summarizes the possibilities (by means of an international literature review); and documents policies, procedures and guidelines in place in Canada and elsewhere (e.g. by ISO, Carbon Leadership Forum, Circular Economy Leadership Canada, CSA); points to the barriers hindering reuse of structural steel in Canada (e.g. certification of old steel) and recommends solutions.
Prerequisites
- TBD: Life-cycle analysis/Sustainability course(s)
Grading Distribution
- Participation in project and research meetings (20%)
- Final Report (80%)
Research Area
Structural Engineering
Faculty Advisors
Professor Shamim Sheikh
Application
Please submit your CV, transcripts and a single paragraph describing your interest in the project in one file within a single email to Prof. Shamim Sheikh shamim.sheikh@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
An extensive research program is underway in the Department of Civil & Mineral Engineering that aims to explore the bond behaviour of GFRP bars with concrete, and its effects on the shear performance of GFRP-reinforced concrete beams through a series of material-level and structural-level tests. MEng students will work in a research group consisting of two PhD students, two MASc students and one postdoctoral fellow, in addition to undergraduate research assistants.
Currently, two MEng project positions are available in this large program. The MEng projects will consist of a subset of the work in the complete research program. There is some flexibility in the choice of the work depending on the interests and expertise of the candidates.
Milestones
Milestone 1: Complete a literature review on the subject. (Months 1-2). Meetings and discussions within the group will provide
accelerated completion of this phase.
Milestone 2: Experimental work (Months 2-6)
Milestone 3: Analyze the test data to assess the performance of specimens, the effects different variables on the behaviour
and design of beams (Months 4-7)
Milestone 4: Produce a final report and present findings (Months 7-8)
Prerequisites
- Knowledge about mechanics, structural analysis, concrete materials and concrete design.
Grading Distribution
- Participation in project and research meetings (20%)
- Assistance with lab work, testing and analysis (50%)
- Completion of academic reports (30%)
Research Area
Structural Engineering
Faculty Supervisor
Professor Jeffrey Packer
External Advisors
Dr. Alex Tinius, Dept. of Ecology & Evolutionary Biology, U of T IDEA StatiCa staff
Application
Interested MEng students should submit one email to Prof. Jeffrey Packer jeffrey.packer@utoronto.ca
with the following documents combined into one pdf file: CV, unofficial transcripts (at U of T and for Bachelor’s or other degrees), one paragraph describing their interest in the project, and one paragraph describing how they have the skills required.
Application Deadline
Rolling
Start Date
Fall 2024 or Winter 2025
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
The pressing challenges facing society today – combating global climate change, saving rare and endangered species, managing exploited resources, comprehending the variation present in the human genome – have their root in ecological and evolutionary forces. Finite element (FE) analysis of three-dimensional shapes formed from structural materials like steel or concrete is commonplace, but FE analysis is also use in bioengineering for the design of human replacement parts and skeletal stress analysis. In this project, leg bones of lizards will be modelled and analyzed, as a cross-disciplinary collaborative venture with the Mahler Lab, in the EEB Department, ESC1026. Dr. Tinius will supply 3D digital models of bones, end constraints and loading conditions, and FE analysis will be performed using IDEA StatiCa software.
This project will investigate why the femur bone of Anolis lizards, shown below, has assumed its particular shape as a load-bearing member in ecologically diverse species. Stress patterns, produced by FE analysis, using the mechanical properties of these bones, will shed light on nature’s evolutionary solution to the bone loadings under compression, bending and shear forces.
Prerequisites
- CIV1174: FiniteElement Method in Structural Mechanics (can be co-requisite)
- Experience with, and aptitude for, structural analysis software
Grading Distribution
- Participation in project and research meetings (20%)
- Final Report (80%)
Research Area
Structural Engineering
Faculty Supervisor
Professor Jeffrey Packer
Application
Interested MEng students should submit one email to Prof. Jeffrey Packer jeffrey.packer@utoronto.ca with the following documents combined into one pdf file: CV, unofficial transcripts (at U of T and for Bachelor’s or other degrees), one paragraph describing their interest in the project, and one paragraph describing how they have the skills required.
Application Deadline
Rolling
Start Date
Winter 2025
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
The design of welded hollow structural section (HSS) connections is governed by a set of specific limit state checks provided in codes/standards, specifications or guides. These design rules cover a limited range of connection arrangements and are subject to limits of applicability, particularly regarding geometry. The complexity of these connection-checking methods has resulted in engineers and steel associations producing software for this purpose, ranging from Excel programs to web-based applications. By examining a range of truss-type HSS connections, manual solutions will be compared to the 3 leading tools in North America with a focus on error detection in each. These are (a) U of T software (Richardson, 2024) complying with CIDECT DG1 & DG3, and CISC Design Modules; (b) HSS Connex by the Steel Tube Institute, complying with ANSI/AISC 360; (c) Bentley Systems “RAM Elements”, also complying with ANSI/AISC 360.
Prerequisites
- Undergraduate steel design course (e.g. CIV312 or CIV518 or equivalent)
- CIV1175: Design of Tubular Steel Structures
- Experience with, and aptitude for, structural analysis software
Grading Distribution
- Participation in project and research meetings (20%)
- Final Report (80%)
Research Area
Structural Engineering
Faculty Supervisor
Professor Oya Mercan
Application
Please submit your CV and a single page describing your background interest in the project in one file within a single email to Prof. Oya Mercan oya.mercan@utoronto.ca
Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
This project focuses on the numerical investigation of diaphragm action in modular steel structures. As the demand for efficient and resilient building systems increases, modular steel structures are becoming a popular choice due to their flexibility and rapid construction. Understanding the diaphragm action, which refers to the behaviour of horizontal structural elements in distributing lateral loads, is crucial for the performance of these structures.
The project will involve detailed finite element modelling and simulation of modular steel structures to evaluate their diaphragm action under various loading conditions. Using ANSYS, a comprehensive suite of tools for finite element analysis, students will develop and validate models to simulate the structural behaviour. Additionally, OpenSees, an open-source software framework for advanced structural analysis, will be employed to perform dynamic analyses and assess the impact of different design parameters. The findings of this project could contribute to the advancement of modular construction techniques, potentially leading to more resilient and adaptive building systems
Milestones
Milestone 1: Complete literature review on diaphragm action in modular steel structures (Months 1-2)
-
- Conduct a comprehensive review of existing research and publications on diaphragm action.
- Identify key factors influencing diaphragm behavior in modular steel structures.
Milestone 2: Develop initial finite element models in ANSYS (Months 3-4)
-
- Create preliminary models of modular steel structures focusing on diaphragm components.
- Validate the models against existing experimental data or case studies.
Milestone 3: Perform dynamic analyses using OpenSees (Months 5-6)
-
- Implement the validated models in OpenSees for dynamic analysis.
- Evaluate the impact of various design parameters on diaphragm action under different loading conditions.
Milestone 4: Analyze simulation results and refine models (Month 7)
-
- Interpret the data obtained from ANSYS and OpenSees simulations.
- Refine the models based on findings to improve accuracy and reliability.
Milestone 5: Develop guidelines and best practices for diaphragm design (Months 8-9)
-
- Synthesize the results into actionable guidelines and recommendations.
- Ensure the guidelines address both theoretical and practical aspects of diaphragm action in modular steel structures.
Milestone 6: Produce a final report and present findings (Month 10)
-
- Compile the research, analyses, and guidelines into a comprehensive final report.
- Prepare a presentation to share the findings.
Prerequisites
- Familiarity with finite element and time-history analyses
- Python programming skills
- Knowledge of structural (steel) design
Grading Distribution
- Participation in project and research meetings (20%)
- Model development and numerical simulation (30%)
- Completion of academic reports (50%)
Research Area
Structural Engineering
Faculty Supervisor
Professor Jeffrey Packer
External Advisor(s)
Idea StatiCa Staff
Application
Interested MEng students should submit one email to Prof. Jeffrey Packer jeffrey.packer@utoronto.ca with the following documents combined into one pdf file: CV, unofficial transcripts (at U of T and for Bachelor’s or other degrees), one paragraph describing their interest in the project, and one paragraph describing how they have the skills required.
Application Deadline
Rolling
Start Date
Fall 2024 or Winter 2025
Course Weight
CIV1002 (Two Half Credit Equivalent)
Project Description
The design of structural steel connections has traditionally been governed by a set of limit state checks provided in codes/standards, specifications or guides, which cover a limited range of connection scenarios. The remainder are left to engineering judgment/experience. In difficult situations, designers may resort to finite element (FE) modelling for guidance on the behaviour and design, but it is usually a very time-consuming and skilled operation using large, complex, commercial FE software packages. Design by FE analysis is, however, now feasible and is covered in Eurocode 3 for Steel Structures by EN 1993-1-14: 2020. Break-through component-based FE design software has been produced by Idea StatiCa to take advantage of this trend, reducing the modelling and analysis time for engineers by orders of magnitude. This software, licensed to U of T, has become instantly popular across the North American structural steel industry but validation is needed against prior rigorous FE models that used non-linear “research quality” software. Validations will use numerical models of welded and bolted hollow structural section (HSS) connections from previous research students, as well as code-based design solutions for comparison.
Prerequisites
- Undergraduate steel design course (e.g. CIV312 or CIV518 or equivalent)
- CIV1175: Design of Tubular Steel Structures (can be co-requisite)
- Experience with, and aptitude for, structural analysis software
Grading Distribution
- Participation in project and research meetings (20%)
- Final Report (80%)
Research Area
Transportation Engineering and Planning
Faculty Supervisor
Professor Khandker Nurul Habib
Application
Please submit your CV and a paragraph describing your interest in the project in one file within a single email to Prof. Habib Khandker.nurulhabib@utoronto.ca Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
Several investigations are warranted regarding the development of next-generation activity-based modelling and these include:
- Weeklong activity planning analysis
- Weeklong telecommuting choice modelling
- Weeklong transit demand analysis
- Weeklong agent-based MATSim modelling
These would make use of one of multiple of the following datasets:
- 2022 TTS data
- 2020 COVHITS data
- 2023 GTTS data
Milestones
To be determined
Prerequisites
CIV531 (taken concurrently if a Fall thesis).
Python or R programming skills are highly desirable.
Grading Distribution
To be determined
Research Area
Transportation Engineering and Planning
Faculty Supervisor
Professor Eric J. Miller
Application
Please submit your CV and a paragraph describing your interest in the project in one file within a single email to Prof. Miller (eric.miller@utoronto.ca). Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
Several investigations analyzing post-pandemic travel behaviour in the Greater Golden Horseshoe (GGH), and identifying significant changes from pre-pandemic conditions are available for students to take on. In each case, 2022/23 TTS and 2016 TTS data will provide the primary sources of information, as well as road and transit network travel times and costs generated by the Travel Modelling Group’s (TMG) GTAModel demand modelling system. The student(s) will work closely with TMG staff and relevant graduate students in the research group. Possible topics include:
- Analysis of day-to day variations in work activity episode generation and working from home (WfH), by occupation group and employment status). Comparison to 2016.
- Analysis of non-work, out-of-home activity/trip generation for WfH & WaH (work at home) workers.
- Analysis of household auto ownership by household socio-economic attributes and residential location.
- Descriptive analysis of bicycle usage: socio-economic attributes, mode shares, trip lengths, spatial distributions, etc.
- Other analyses of travel behaviour change that can be supported by TTS data.
Milestones
To be determined
Prerequisites
CIV531 (taken concurrently if a Fall thesis).
Python or R programming skills are highly desirable.
Grading Distribution
To be determined
Research Area
Transportation Engineering and Planning
Faculty Supervisor
Professor Marianne Hatzopoulou
Application
Please submit your CV and a paragraph describing your interest in the project in one file within a single email to Prof. Marianne Hatzopoulou, marianne.hatzopoulou@utoronto.ca Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling, accepting 2 positions
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
The Transportation and Air Quality research group (TRAQ) is collecting air pollution data using UrbanScanner, a mobile platform designed to measure air quality and collect pictures of the urban environment. This project entails matching air quality measurements with images, analyzing important features of the images (the ones that are typically associated with air pollution such as building height, number of vehicles, trucks, trees), and developing models that predict air quality based on these features.
Milestones
This section is optional, but it would be good to include it if you can provide more granularity about how the project will progress. This example is for a year-long project:
Milestone 1: Month 2- literature review and proposed methodology
Milestone 2: Month 4- image analysis and database development
Milestone 3: Month 6- final models
Milestone 4: Month 8- final report and presentation
Prerequisites
- CME538 – Introduction to Data Science
- Python programming skills and Geographic Information Systems (GIS)
- Introductory knowledge of image analysis
Grading Distribution
- Participation in project and research meetings (20%)
- Assistance with data collection and analysis (30%)
- Final report and presentation (50%)
Research Area
Transportation Engineering and Planning
Faculty Supervisor
Professor Khandker Nurul Habib
Application
Please submit your CV and a paragraph describing your interest in the project in one file within a single email to Prof. Habib Khandker.nurulhabib@utoronto.ca Shortlisted applicants will be contacted thereafter.
Application Deadline
Rolling
Start Date
Flexible
Course Weight
CIV1001 (One Half Credit Equivalent) or
CIV1002 (Two Half Credit Equivalent)
Project Description
The project will involve working on developing a Smartphone app component of the travel survey software developed at the U of T, TRAISI. The tasks will involve working with a PhD student in helping with coding, testing prototypes and field testing
Milestones
To be determined
Prerequisites
- CIV531 (taken concurrently if a Fall thesis).
- App development experience.
- Python or R programming skills are highly desirable.
Grading Distribution
To be determined