Civil Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10315/34418

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Access status: Open Access ,
Biofilm Control Inside Secondary Water Storage Containers Using UV LIght
(2026-03-10) Di Falco, Patrick Alexander; Gora, Stephanie
This thesis investigates the feasibility of ultraviolet (UV) light treatment for biofilm control inside secondary storage containers used in humanitarian settings. A scoping literature review investigating real‑world UV light treatment application revealed that, even though it is effective beyond controlled laboratory settings, it is underexplored as a method for biofilm control in humanitarian contexts. To address this gap, a ray tracing model was developed to simulate UV irradiance distribution within a representative jerry can, and experimental validation showed model predictions were within 17% of measured values. Lab-grown biofilms were then treated at locations inside the jerry can receiving moderate and low levels of UV irradiance. At a maximum UV dose of 16 mJ/cm², the location receiving moderate UV irradiance achieved 2.47 ± 0.75 (average ± standard deviation) log-reduction value (LRV) while the location receiving low UV irradiance achieved 2.99 ± 0.07 LRV. Comparisons with other UV/biofilm research showed that similar germicidal thresholds could be achieved even with the lower parameters incorporated into this research. These findings demonstrate that UV light treatment is a technically viable method for biofilm control inside secondary storage containers used in humanitarian settings. Future research investigating this treatment method under real world conditions will prove to be the next key step in determining its feasibility for future implementation in humanitarian contexts.
Access status: Open Access ,
Addressing Zoning Abbreviation Inconsistencies in the Greater Toronto Area (GTA) through a Universal Zoning Ontology
(2026-03-10) Mesbahian, Arian; Jadidi Mardkheh, Amaneh
Urban development in the Greater Toronto Area (GTA) is increasingly constrained by inconsistencies in zoning abbreviations and regulatory terminology across municipal jurisdictions. Zoning labels that appear similar, such as “R2” in the City of Toronto and “R2 S” in the City of Markham, often represent different land use permissions and development standards. These semantic discrepancies create confusion for planners, developers, and regulators, leading to inefficiencies, legal ambiguity, and prolonged approval timelines. At the same time, Ontario faces a significant housing supply crisis. Despite the provincial target of delivering 1.5 million new homes by 2031, housing production continues to lag, with projections estimating only 81,300 units in 2024. Contributing factors include limited servicing capacity for water, wastewater, and stormwater infrastructure, as well as fragmented and slow municipal approval processes. When combined with inconsistent zoning terminology, these challenges further delay housing delivery and undermine coordinated regional planning. To address this systemic issue, this research introduces Zonology, a machine readable, ontology-based framework designed to semantically standardize zoning terminology across municipalities. Using the City of Toronto and the City of Markham as proof of concept case studies, Zonology harmonizes over 60 zoning categories and aligns more than 150 permitted land uses within a shared semantic structure. Zonology formally models zoning designations, permitted land uses, development standards, and spatial relationships by integrating municipal zoning bylaws, planning regulations, and geospatial data. The framework supports semantic querying and interoperability with Geographic Information Systems (GIS), automated planning workflows, and smart city applications. By resolving semantic and regulatory fragmentation, Zonology enhances data driven decision making, improves inter municipal collaboration, and provides a scalable foundation for consistent, future ready zoning governance in the GTA and other multi-jurisdictional regions.
Access status: Open Access ,
A Multi-Stage Optimization Framework for Battery Swapping in Urban E-Bike Sharing Systems
(2026-03-10) Farshchi Heydari, Fatemeh; Nourinejad, Mehdi
Shared electric bicycles (e-bikes) are increasingly central to urban transportation, providing a faster, more accessible alternative to conventional bicycles for short trips. As their use grows, maintaining sufficient battery levels across large fleets becomes critical. Among battery management strategies, battery swapping, which replaces depleted batteries with fully charged ones, offers a scalable solution, especially for dockless systems that lack fixed charging stations. However, it presents logistical challenges related to vehicle routing, battery delivery, and capacity constraints. This thesis introduces an optimization framework that combines dynamic clustering with a dual-objective vehicle routing model. E-bikes requiring service are grouped into van-feasible clusters based on battery level, spatial proximity, and fleet availability. Each cluster is then optimized to minimize travel distance while maximizing economic return. The framework is applied to real-world data from San Francisco’s Bay Wheels system, accessed via the General Bikeshare Feed Specification (GBFS). Results show that the proposed method reduces travel distance and enhances the efficiency of battery-swapping operations.
Access status: Open Access ,
Cyber-security Aware Traffic Flow Modeling and Data Processing Power Optimization
(2026-03-10) Khalajiolyaie, Mahdiye; Nourinejad, Mehdi
The growing deployment of connected and automated vehicles (CAVs) introduces new opportunities and challenges at the intersection of traffic engineering and cybersecurity. While CAVs leverage onboard sensors and computing to navigate their environment, the integration of vehicle to vehicle (V2V) and vehicle-to-infrastructure (V2I) communications has elevated expectations for cooperation, safety, and efficiency. This connectivity, however, introduces system-level vulnerabilities that are not present in isolated autonomous vehicles. In this thesis, we develop a queueing-based analytical framework to examine how cyber-induced communication loads and adversarial message infiltration influence the real-time processing of detection and control signals within CAVs. By formulating and solving optimization problems for resource allocation, we identify how limited computational capacity should be divided between detection and decision-making tasks to maintain traffic performance in adversarial environments. The model derives closed-form relationships between processing rates and macroscopic traffic variables such as delay, flow, and speed. Numerical experiments reveal critical trade-offs, as malicious message share increases, the system must prioritize defensive processing at the cost of responsiveness, leading to changes in traffic efficiency. Our findings emphasize the need for cybersecurity-aware traffic flow models and provide operational insights into the design of resilient and adaptive cooperative driving systems.
Access status: Open Access ,
Coupling Geothermal Heating with BTEX Bioremediation in the Subsurface
(2026-03-10) Kaur, Gurpreet; Brar, Satinder Kaur
There has been a worldwide interest in renewable energy technologies as a means of reducing reliance on fossil fuels, mitigating the effects of climate change, and reducing greenhouse gas emissions. One such technology is geothermal heating, where the constant subsurface temperature is used to cool or heat building interiors via heat pumps. In Canada, the use of geothermal heat pumps (GHPs) has become a popular option for heating and cooling buildings. It is anticipated that, in the near term, most large buildings will incorporate GHPs as part of their climate control strategy. However, little is known about the environmental impacts of geothermal heating on the subsurface environment. The present thesis examined the effect of geothermal heating on groundwater flow and remediation efforts, whereby the heat generated by geothermal systems may aid in addressing urban pollution. "Geothermal remediation" could leverage the subsurface heating resulting from geothermal systems to accelerate biodegradation of certain petroleum-based pollutants at brownfield sites, while providing building(s) with sustainable heating and cooling. This idea coincides with the rising momentum towards sustainable and green remediation in Europe and the United States. To ensure that Geothermal remediation is achievable, the effect of heat on bioremediation needs to be examined. This research investigated the heat effects on the bioremediation potential of pure culture and consortia and their potential for (Benzene, Toluene, Ethylbenzene and Xylene(s)) BTEX degradation as a pollutant. In the present thesis, soil microorganisms with the potential to degrade BTEX were isolated using an enrichment method from soil samples collected at different depths from geothermal boreholes. The microbes were screened and optimized for BTEX degradation at three different temperatures (15, 28 and 40 °C). The bacterial strains Microbacterium esteraromaticum and Bacillus infantis exhibited the highest degradation compared to other isolated strains and the reference strain, Pseudomonas putida. All four BTEX compounds were metabolized 2 times faster at 28 °C and 40 °C. Metabolomics data showed that BTEX was metabolized entirely to acetaldehyde and carbon dioxide by these selected strains. The catechol 1,2-dioxygenases, catechol 2,3-dioxygenases, and toluene monooxygenase enzyme activity confirmed the tol and tod degradation pathways. Furthermore, the present work offers new insights into the responses of soil microbial communities to electron acceptors under anoxic conditions, indicating that intrinsic microorganisms can be successfully stimulated for in-situ bioremediation (ISB) with electron acceptors as a supplement. The investigation revealed a maximum BTEX biodegradation of 57% by B. infantis under sulfate reduction and overall, 98% by M. esteraromaticum in combined nitrate and sulfate reduction. To understand the soil matrix influence and to mimic geothermal heating effects, small-scale soil batch experiments and continuous soil column experiments with cyclic temperature were performed. The results revealed that cyclic temperature of 5 °C to 40 °C (shallow low enthalpy geothermal temperature range) enhanced the BTEX biodegradation by 2-fold in silty loam soil (> 80%) in comparison to constant aquifer temperature (12 °C) (40%). Finally, a metagenomics study was performed on soil samples from different depths at three temperatures (15, 28, and 40 °C) to provide insight into how geothermal heat could impact the soil microbiome and its effect on bioremediation activities. Potential known strains for BTEX biodegradation such as Pseudomonas, Arthrobacter, Bacillus, as well as some novel strains such as Microbacterium, Janthinobacterium, Methylotenera, were found to be dominant at 28 °C and 40 °C. Since microbial abundance and diversity decreased drastically at 15 °C; these findings showed the potential of geothermal heating as a sustainable heat source for ISB of pollutants.
Access status: Open Access ,
Thermal Effects on Concrete Bridges in a Changing Climate
(2026-03-10) Saad, Saad; Bashir, Rashid; Pantazopoulou, Stavroula
The research presented herein aims to: 1) Investigate the suitability of AASHTO Bridge Design Specifications in quantifying the design thermal gradients that account for cold wave events, 2) Study the effect of different climate parameters on thermal gradients to help improve current guidelines and ensure that thermal gradients are derived based on the actual bridge location, 3) Quantify the effect of freezing temperature on the coefficient of thermal expansion (CTE) of concrete, 4) Analyze the structural response of a bridge structure to cold wave events, while considering the effect of temperature on the magnitude and sign of the CTE of concrete, and 5) Investigate the effect of climate change on thermal load. The objectives of this work were achieved mainly using numerical finite element 3D models. Furthermore, the relationship between sub-freezing temperature and thermal strain was studied through experimental testing of concrete cylinders. A weather generator was used to simulate future climate conditions to study the impact of climate change on thermal loads. The findings indicated that current guidelines fail to capture the true thermal load distribution within a bridge superstructure, which leads to an underestimation of the resulting structural implications, particularly the tensile stresses at the bottom of the cross section. It was also determined that a correlation exists between the direct normal irradiance at a specific location and the resulting thermal differential in a bridge. In addition, the effects of subfreezing temperature on the CTE of concrete were found to be significant and to strongly impact the structural behavior of bridges under cold wave events. For instance, a significant increase in tensile stress in both transverse and vertical direction was predicted during a high intensity cold wave event, an issue which can cause concrete cracking. Furthermore, a methodology to model future hourly climate data was developed, through which it was determined that climate change will have considerable effects on thermal loads on bridges in the future. For example, it was determined that climate change can cause an increase of about 5℃ and 6℃ in the absolute maximum positive thermal differential in bridges located in Toronto and Whitehorse respectively.
Access status: Open Access ,
Studying the Effect of Hydraulic Hysteresis with Air Entrapment on Solute Transport and Slope Stability Under Different Climatic Conditions
(2026-03-10) Moustafa, Moamenbellah Mohye Abdelhamid Elsayed; Bashir, Rashid
Hysteresis, a natural soil phenomenon, manifests distinct hydraulic responses influenced by the soil-water characteristic curve (SWCC), reflecting soil pore paths during infiltration and drainage. This study investigates the interplay between hysteresis and air entrapment, essential for accurate hydrological modeling under intermittent water flow. Despite their significance, numerical models often overlook hysteresis, relying on nonhysteretic curves. This research explores the impact of hysteresis with air entrapment on solute transport and slope stability in diverse scenarios. Comparisons between hysteretic and nonhysteretic analyses reveal increased water fluxes and deeper solute migration when considering both hysteresis and air entrapment. Neglecting these factors leads to inaccurate solute fate and slope stability assessments. In slope stability analysis, air entrapment significantly lowers suction strength and factor of safety, potentially triggering slope failures. This thesis establishes the critical importance of considering both hysteresis and air entrapment for robust hydrological and geotechnical assessments.
Access status: Open Access ,
Development, Material and Structural Performance of Tension Hardening Fiber Reinforced Geopolymer Concrete (THFRGC)
(2026-03-10) Ralli, Zoi Georgios; Pantazopoulou, Stavroula
On account of growing environmental and economic concerns, decarbonization of the concrete industry has become a priority with the development of environmentally friendly building materials to attract both research community and industry. A class of advanced eco-friendly building materials is geopolymer concretes. Their production incorporates industrial by-products in lieu of cement which has a double benefit in terms of sustainability: recycling industrial wastes instead of harmful disposal and reducing carbon footprint by eliminating cement. Meanwhile, Tension-Hardening Fiber Reinforced Concrete (THFRC) shows great potential as a structural material for modern infrastructure due its enhanced tensile strength and ductility. Although THFRC is considered a sustainable solution thanks to the tension-hardening delaying the need for retrofits, the high amount of Ordinary Portland Cement (OPC) used as a binder raises concerns regarding the sustainability of the material hindering the widespread application on account of the high cost and carbon footprint. This dissertation aims to advance the knowledge on sustainable and high-performance building materials by developing and characterizing a Tension-Hardening Fiber Reinforced Geopolymer Concrete (THFRGC) in terms of its material and structural behaviour. After a thorough review of the related literature, the experimental stage comprises the characterization of various mineral powders, mix design based on chemical and physical optimization, material identity characterization according to North American Standards prescribed for conventional THFRCs, determination of bond-slip law of reinforcing bar in THFRGC and performance under biaxial stress states. To promote the widespread use of the material, rheology, and fiber orientation in THFRCs are also explored using destructive and non-destructive techniques. Furthermore, this project aims to tackle the conundrum of tensile characterization of THFRC by developing a novel indirect tension technique that combines the simplicity of splitting test with the ability to obtain the whole response in pure tension. The proposed test is numerically validated using advanced Non-Linear Finite Element Analysis. The latter was also employed to investigate the validity of the tensile response obtained using the Inverse Analysis proposed by CSA, S6, Annex 8.1. Finally, self-sensing performance of a multifunctional nanoengineered THFRC is explored to pave the way for smart THFRCs in Structural Health Monitoring (SHM) applications.
Access status: Open Access ,
Subscription & Per-Day Pricing vs Same-Day Service: A Simulation of Temporally Consolidated Delivery Systems
(2025-11-11) Jeoung, Won Mo; Nourinejad, Mehdi
Same day delivery is often viewed as the benchmark for last mile logistics because of its convenience and time savings, but its adoption as a wide spread service is limited by high costs. Orders vary between time and location, restricting economies of scale and often resulting in inefficient routes where multiple vehicles serve the same neighborhood within short intervals. To address these issues, this thesis introduces temporal consolidation, where deliveries are grouped and scheduled for specific days of the week or month. Using simulations of customers with different order behaviors and locations, we evaluate delivery scenarios that include grouping requests, optimizing travel routes, and adjusting delivery frequencies. Results show that consolidated deliveries reduce total travel distance compared with same day service. Customers are generally willing to accept longer consolidation periods when paired with cost savings, while higher delivery prices increase demand for faster service.
Access status: Open Access ,
Data-Driven Bike-Share Ridership Prediction and Network Optimization
(2025-11-11) Mohseni Hosseinabadi, Ghazaleh; Park, Peter; Nourinejad, Mehdi
Shared micro-mobility systems, particularly station-based bike-sharing networks, have become key components of urban transportation, yet their planning remain challenged by spatial and technological complexities. This dissertation develops integrated models for ridership prediction, station placement optimization, and electrification planning to address these challenges. First, a customized Graph Neural Network framework using GraphSAGE is introduced for station-to-station ridership prediction, integrating network topology, sociodemographic features, and station attributes. Applied to Toronto, the model outperforms linear, spatial, and tree-based benchmarks, demonstrating its ability to capture latent dependencies and support demand-responsive planning. Second, a continuum approximation model is proposed for station placement optimization, using a force-based algorithm that balances attraction from demand centers with inter-station forces. This ridership-driven approach departs from conventional accessibility methods by directly aligning locations with demand. Applied to Vancouver, the model reveals optimal spacing patterns and highlights strategies for ridership-driven network expansion under varying demand conditions. Third, the dissertation extends infrastructure planning to electrified systems by introducing a two-dimensional Markovian state-of-charge framework for e-bikes. A heuristic charger deployment algorithm, enhanced by a single-pooling state approximation, maximizes expected ridership and identifies high-impact charging locations, achieving near-optimal performance in case studies from Pittsburgh, Vancouver, and San Francisco. Finally, the models are integrated into a web-based, GIS-enabled decision-support tool that combines predictive, prescriptive, and descriptive analytics to enable scenario-based planning. Demonstrations in Toronto and Vancouver illustrate the tool’s scalability and practical value. This research advances methodological foundations and practical tools for developing resilient, data-driven, and electrified bike-sharing networks across diverse urban environments.
Access status: Open Access ,
Signal timing for LCV trucks on a road network using reinforcement learning
(2025-11-11) Ghanbari Sefiddargoleh, Mohammad; Gingerich, Kevin
Freight activity in urban networks is rising, and jurisdictions such as Ontario are encouraging the use of Long Combination Vehicles (LCVs) to consolidate freight loads. This thesis quantifies the delays and queueing on 16 intersections in the Region of Peel and introduces an adaptive signal-control strategy. Tested scenarios include (1) existing signal timing plans without LCVs and (2) with LCVs, (3) a single-intersection double deep q-network (DDQN) controller without LCVs and (4) with LCVs. Introducing LCVs under existing signal timings raised network-wide delay by 14 % for all vehicles and 22 % for trucks when LCVs comprised just 1.7 % of traffic. The proposed DDQN was found to reduce average delays for all vehicles and trucks based on various conditions. Future work should extend the single intersection approach to a multi-agent framework and explore continuous-time action spaces for even finer control.
Access status: Open Access ,
Surface Infrastructure Improvement for Efficient Long Combination Vehicles and Truck Platooning Operation
(2025-11-11) Akkeh, Jowel; Park, Peter Y.
The growing demand for freight transportation has led to increased congestion on urban arterial roads, requiring innovative solutions such as long combination vehicles (LCVs) and heavy commercial vehicle (HCV) platooning. However, these approaches face challenges at intersections due to limited green time and insufficient lane storage for left turns. This study examines the use of Intelligent Transportation Systems (ITS) to enhance truck travel time, specifically through Freight Signal Priority (FSP) and dedicated truck left-turn lanes (DTLL). A methodology was developed to identify intersections requiring truck priority measures. Using PTV VISSIM, a micro-simulation model was created for a 19.2 km corridor with 32 signalized intersections. Freight vehicle composition included 5% LCVs and single-unit trucks. Eight models were used for comparative analysis. Model 1 represents the 'do-nothing' scenario that includes the assumption of 5% LCVs, Model 2 applies the FSP scenario, Model 3 includes the DTLL scenario, and Model 4 combines Models 2 (FSP) and 3 (DTLL). Model 5 assessed a 5% penetration rate of HCV Platooning under existing conditions. Model 6 adds FSP to Model 5, Model 7 adds DTLL to Model 5, and Model 8 combines Models 6 (FSP) and 7 (DTLL). The study found that implementing both FSP and DTLL together yields better results than applying each individually. This integrated approach demonstrated improvements in efficiency, traffic flow, and sustainability by reducing travel time and greenhouse gas (GHG) emissions for all vehicles.
Access status: Open Access ,
Numerical Modelling for Alternative Solution Designs of Steel and Timber Structures in Fire
(2025-11-11) Chin, Kathryn Leigh; Gales, John A.
The objective of this thesis is to increase confidence in alternative solution designs, since its use has increased. Through experimental data collection, a better understanding of steel and timber behaviour in fire was gained and the knowledge was transferred to numerical model development. A validated modelling methodology allows designers to predict structural performance in fire which can then be used to justify decision making and as a method to show the design is code compliant. This thesis examined the performance of steel trusses exposed to a localized fire with a complementary heat transfer and thermal deformation model. For timber, cone calorimeter experiments were completed on various timber products with complementary heat transfer models followed by an exploratory study on thermal parameters in modelling. The thesis provides recommendations for best practices and future research regarding finite element modelling of steel and timber to further develop tools for alternative solution designs.
Access status: Open Access ,
Understanding Water Quality Interactions Within a Decentralized Drinking Water System in a Community in Nunavik, Canada
(2025-11-11) Mahgoub, Ammar Yasir; Gora, Stephanie
This study examined microbial water quality in a subarctic Canadian community reliant on a decentralized truck-to-cistern system. Samples were collected from 40 residential cisterns and various stages of the local water treatment plant (WTP) to measure adenosine triphosphate (ATP), an indicator of microbial activity. Non-parametric statistical tests addressed five research questions related to spatial variation, building age, WTP effectiveness, correlations with water quality parameters, and cistern configuration. No significant differences were found between cisterns across different zones or construction periods. However, ATP levels varied significantly across WTP stages, and cleaning had a measurable impact on microbial activity. Moderate correlations between ATP and turbidity were also identified. While results support improved monitoring practices, the study’s limited sampling and single-community focus restrict broader generalization. Future work should expand sampling duration, include biofilm analysis, and utilize pilot cisterns and digital twins to better understand and manage microbial risks in truck-to-cistern systems.
Access status: Open Access ,
Scalable Urban Crowdsensing: Data Contributor and Consumer Dynamics
(2025-11-11) Heydarigharaei, Elham; Nourinejad, Mehdi
Dynamic transportation routing and parking management rely on real-time data on traffic conditions and curbside availability. Traditional data collection methods require significant infrastructure investments and processing capacity, making them costly and often impractical for large-scale implementation. In contrast, crowdsensing leverages data from users’ smart devices, offering a cost-effective alternative for collecting real-time information. However, crowdsensed data is often noisy, inaccurate, unstructured, and heavily dependent on voluntary user contributions. This thesis is motivated by the central question: how can we effectively design and manage platforms, such as parking management and routing, that rely on such incomplete yet abundant data? To address this, several key studies are presented. The first study, Leveraging Data Contributors to Enhance Social Welfare Through Crowdsensing, examines how user-generated data can improve decision-making at service facilities such as parking lots. By aggregating reported wait times, the platform provides real-time estimates that help incoming users decide whether to enter. This research determines the optimal fraction of data contributors needed to maximize the collective benefit, social welfare, of facility users. The second study, Route Choice Using Crowd-Generated Travel Time Information, explores how crowdsensed travel times influence traffic assignment. Two critical factors, contribution ratio (the proportion of travelers sharing data) and observation window (the period over which data is collected), must be balanced to minimize network-wide travel times. This study analyzes their effects on perceived travel risks and proposes solutions to maintain their optimal values, ensuring efficiency in routing decisions. The third study, Resource Allocation and Route Generation for Urban Mobile Sensing, focuses on optimizing the number and routes of sensing agents, users carrying data collection devices, to enhance efficiency. By determining the optimal number of agents and their navigation paths, this study minimizes total travel costs while ensuring that each parking spot is revisited within the specified headway constraints. A case study in Toronto demonstrates the practical applicability of the proposed optimization framework for parking occupancy detection. The fourth study, Implementing Parking Occupancy Detection Using Dashcam Footage, develops a sensing system that analyzes dashcam video to detect real-time parking occupancy. By applying advanced video processing techniques, this project provides dynamic and scalable insights into urban parking availability. It aims to overcome the limitations of traditional parking data collection tools, such as stationary cameras and sensors, which face challenges related to installation, maintenance, and regulation. Instead, mobile technologies like dashcams, LiDAR, and ultrasound sensors offer a more scalable solution for capturing on-street parking availability. Together, these studies contribute to improving the efficiency of crowdsensing platforms by analyzing the impact of three types of crowdsensed data: wait times, travel times, and urban monitoring reports. These data types are examined across three distinct domains: service facilities (e.g., parking facilities), transportation networks (e.g., scenarios involving multiple modes of transportation or alternative routes with varying congestion levels), and urban monitoring (e.g., parking availability detection, pedestrian safety, and traffic flow monitoring). The research examines how access to such data influences individual decision-making, particularly in the presence of competing alternatives, as well as its impact on overall system performance. Furthermore, it investigates the factors affecting the accuracy and reliability of crowdsensed data in each domain and explores how optimizing these factors can enhance both user experience and system-wide efficiency.
Access status: Open Access ,
Microplastics Transport in Turbulent Flow: Investigating the Effects of Physical Characteristics and Flow Dynamics
(2025-11-11) Shamskhany, Arefeh; Karimpour, Shooka
The surge in global plastic production has led to increasing plastic pollution in aquatic environments, where plastic debris fragments into microplastics (MPs), particles smaller than 5 mm, through weathering processes. MPs are transported by ambient flow across different aquatic compartments, posing ubiquitous risks to the ecosystem health. Effective mitigation of MPs' risks requires a comprehensive understanding of MPs' transport and mobility. Turbulence and the natural settling or rising movements of MPs are fundamental transport mechanisms, yet many aspects of how MPs' diverse physical properties affect these processes remain underexplored. Density, size, and shape are amongst critical physical properties of MPs that shape their transport and affect flow interactions. This PhD dissertation investigates the effects of MPs’ physical properties on their transport and mixing in turbulent flows using numerical and experimental approaches. The findings of this research elucidate how density, size, and shape affect the behaviour of MPs, providing explanations for their selective abundance and distribution in aquatic environments. Results of this PhD dissertation illustrate that lower marginal densities relative to the ambient fluid, smaller sizes, and non-spherical shapes make MPs more susceptible to the transient dynamics of the ambient flow as such MPs deviate significantly from their terminal behaviours. The findings explain the distant transport of smaller non-spherical MPs and the absence of smaller MPs of common polymers in offshore surface layers, as such particles are more likely transported to deeper water columns by in-depth currents. This research also explores the advantages of dynamic Lagrangian modelling over commonly used kinematic approaches, emphasizing the importance of particle acceleration for MPs with higher mixing levels, particularly those with smaller sizes, lower marginal densities, and non-spherical shapes. These findings contribute to understanding MPs' transport and distribution based on their physical properties and flow dynamics and offer a foundation for developing effective strategies to mitigate the ecological impacts of MPs.
Access status: Open Access ,
Parametric Design of Multi-Echelon Last-Mile Delivery Systems Using Novel Technologies
(2025-07-23) Dehqani Viniche, Bahar; Nourinejad, Mehdi
The growing demand for fast and cost-effective last-mile delivery has prompted logistics operators to explore emerging delivery technologies such as autonomous vehicles (AVs) and drones. This dissertation investigates the design and strategic planning of multi-echelon last-mile delivery systems using emerging delivery technologies. Through three complementary studies, we explore the integration of AVs and drones into traditional truck-based delivery networks using analytical models and parametric design frameworks. The first study examines recipient-dependent routing policies in AV-enabled deliveries, where recipients participate by using their AVs for parcel pickup. We introduce and analyze three delivery policies- AV pickup, Hybrid pickup, and Crowdsourced delivery— and derive upper and lower bounds on their total system costs. These policies are compared against the traditional truck delivery model, with results showing that recipient-dependent strategies become increasingly dominant as the number of deliveries grows. The second study extends this concept to the delivery of perishable goods, where delivery time is critical. Using parametric design, we introduce length-constrained (time-sensitive) routing models and compare AV, Hybrid, and crowdsourcing policies to traditional truck delivery. Our findings show that AV integration offers significant advantages in reducing delivery failure for perishable goods, particularly under increasing hand-off costs and high delivery density. The third study focuses on tactical fleet planning and routing in drone-assisted last-mile delivery. We model a mixed fleet of trucks and drones, considering drone range, truck and drone capacities, multi-launching, and vehicle synchronization. Using continuous approximation, we derive closed-form solutions for fleet composition and routing strategies under varying operational scenarios. The analysis yields policy spaces that guide optimal system design based on drone and truck operational parameters, such as vehicle speeds and capacity limits. This dissertation, through its integrated studies, offers a unified framework for the strategic design and operational evaluation of multi-echelon last-mile delivery systems using emerging delivery technologies. The models developed provide managerial insights for logistics planners, policymakers, and researchers seeking scalable and efficient delivery solutions.
Access status: Open Access ,
Pre-emptive and Reactive Resource Allocation in Emergency Response
(2025-07-23) Khaksar, Ali; Nourinejad, Mehdi
Emergency response departments face significant challenges due to resource scarcity and fluctuating incident demands. Traditional static resource allocation, assigning resources to fixed stations, often results in inefficiencies, leaving resources underutilized or insufficient in high-demand areas. To address these shortcomings, this research develops a dynamic resource allocation framework tailored specifically for fire departments. The framework integrates preemptive strategies based on historical data and predictive modeling with reactive strategies responding to real-time emergencies. Validated through a case study in Fredericton, Canada, using data from 2017 to 2021, results demonstrate that dynamic allocation significantly reduces response times and enhances resource efficiency compared to static methods. This study provides a data-driven, practical approach to optimizing resource management in fire departments, effectively addressing operational constraints such as geographic challenges, varying demands, and limited resource availability, thereby improving efficiency, readiness, and effectiveness in emergency response operations.
Access status: Open Access ,
A Safety Analysis of Left-Turning Maneuvers for Long-Combination Vehicles
(2025-04-10) Marchesan, Luciano; Gingerich, Kevin
A growing number of goods are shipped in Ontario using long-combination vehicles (LCVs). LCVs can significantly increase the amount of cargo carried per shipment, while reducing shipping costs and environmental impacts of freight road travel. Questions remain regarding their safety due to their larger size and wider maneuvering. This thesis furthers our understanding of LCV safety considerations in last-mile areas during left-turning maneuvers by focusing on conflicts with infrastructure and road users. Intersections are selected along Peel Region’s Strategic Goods Movement Network and left-turning swept path analyses are performed using AutoTurn. A microsimulation of the intersections is developed, where the existing network is modeled in Vissim while LCVs are added as potential road users. The Surrogate Safety Assessment Model is then used to analyze the microsimulation results pertaining to potential collisions. It was found that existing last-mile infrastructure is ill-equipped to accommodate LCVs and requires adjustments for future LCV use.
Access status: Open Access ,
Design and Installation of Ultra-High Performance Fiber Reinforced Precast Jackets for Retrofitting Concrete Piers
(2025-04-10) Salazar Gonzalez, Roberto; Pantazopoulou, Stavroula
Ultra-high performance fiber-reinforced concrete (UHPFRC) has emerged in the last decade as a new alternative material for retrofitting reinforced concrete structures such as buildings and bridges. One of the most common applications of UHPFRC today is restoring bridge decks and construction joints. UHPFRC can also be applied to the retrofit of principal structural components, such as beams and columns. Decks are repaired by the addition of UHPFRC overlays, whereas columns can be retrofitted with UHPFRC by applying a new layer on the original cross-section, or by removing the concrete cover and replacing it with UHPFRC. This technique is known as jacketing, and it is an attractive solution due to the outstanding mechanical properties of UHPFRC and the fact that the retrofit may be limited to the length of the critical regions and does not have to cover the total height of the column. Specifically, with regards to UHPFRC Jacketing, research on UHPFRC has been conducted when used as a cast-in-place solution. In this research, UHPFRC jacketing is studied as a seismic retrofit alternative, to quantify the jacket's contribution to the various mechanisms of resistance (flexure, shear, reinforcement anchorage capacity, and the confinement effectiveness of the jacket in enhancing the strength and deformation capacity of encased concrete). With confinement effectiveness being the primary knowledge gap, the study included an extensive experimental component, where rectangular concrete prisms modeling concrete columns were jacketed with precast UHPFRC jackets. The objective of the experimental study was to test the proof of concept of jacketing with precast jackets made of UHPFRC, and to also shed light on the contribution of the jackets to the deformation capacity of encased concrete in order to quantify the jacket's effectiveness. As part of the research, the method of connection of the jacket segments and characterization of the materials were designed and optimized through testing. Two possible connectors were designed and tested, providing good results. Several columns were tested under monotonic and cyclic loading, after assembling the precast jackets designed and fabricated for the needs of the study. Parameters of investigation included two jacket thicknesses (i.e. 25 mm and 37.5 mm), the type of connectors between precast components, the loading method (monotonic or cyclic compression load on the encased concrete) and the method of jacketing (cast in situ vs. attaching precast jackets). Finite element modeling was included to understand the lateral resistance to dilation of the encased concrete imparted by the jackets. The deformation capacity of the columns was enhanced substantially. The findings in this research aim to provide a valuable understanding of UHPFRC as a retrofit solution in the form of jacketing.

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