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Agent-based simulation to evaluate the impact of seismic retrofit promotion policies
No full textSeismic risk is a global concern, as buildings around the world are vulnerable to damage or collapse during earthquakes. Recent developments in engineering design have resulted in substantial improvements to the seismic performance of new buildings. However, buildings constructed before recent upgrades to the seismic regulations comprise a large portion of the existing building inventory. Buildings with insufficient seismic capacity are susceptible to extensive damage or collapse during an earthquake, contributing to economic losses and casualties. Effective risk mitigation strategies such as seismic retrofitting could potentially address the vulnerability of the existing building stock to earthquake hazards. Yet, seismic retrofit programs suffer from low take-up rates. Thus, identifying barriers for seismic retrofit adoption and strategies to increase take-up rates can help mitigate losses from future events. This study develops an agent-based model to assess homeowner response to multiple seismic retrofit promotion strategies including running education campaigns or providing retrofit cost subsidies. The simulation framework is applied to a case study of owner-occupied, residential-detached dwellings in Vancouver, British Columbia, Canada, to evaluate the effectiveness of various potential seismic retrofit promotion strategies. These strategies are compared regarding the number of adopters and the reduction in total annual losses to residential building structures and contents in the City of Vancouver.
The results suggest that a combination of educational programs and retrofit cost subsidies for low-income households yield the highest adoption rates and the largest reduction in total annual losses to detached residential dwellings. However, the results show focusing only on an educational campaign can achieve similar results in terms of adopters and loss reduction. The model highlights that risk perception is the main barrier for moderate and high-income groups, while the combination of risk perception, willingness to pay, and perceived benefits (tangible financial returns) are the factors impeding the adoption of retrofit measures among low-income homeowners. The analysis on the effectiveness of retrofit promotion policies in reducing earthquake-induced annual losses suggest that government investment in education or subsidies could be cost-effective provided that the implementation costs remain below the projected savings in avoided losses. The model results could be used to inform local government what would be the viable annual budget to be spent on seismic risk reduction strategies. A more comprehensive cost-benefit analysis is needed, involving collaboration between public agencies, insurers, and the construction industry to quantify implementation costs, co-benefits, and risk-sharing mechanisms. This study contributes both methodologically and quantitatively to the development of effective seismic risk mitigation policies. By modelling policy impacts before implementation, policymakers can better evaluate and refine strategies to minimize damage and losses in future seismic events
Methods for Modelling Wetlands in Hydrologic Models
No full textWetlands are abundant natural systems that serve as important ecosystems, mechanisms for nutrient filtering and storage, and providers of flood mitigation services. Wetlands strongly influence the hydrologic response and water balance on a landscape. The practice of water resources management often relies on numerical computer models that represent hydrologic features within a watershed like wetlands, lakes, and rivers, to accurately simulate the movement of water. However, representation of wetlands in hydrologic models is challenged by their small-scale nature, numerous classification schemes that are not readily associated with a water balance conceptual model, and sometimes complex hydrology. A shortcoming of existing wetland modelling studies includes the lack of multiple wetland types being represented, often due to the complexity that accompanies wetland classification schemes.
In this study, we address three research objectives: 1) to inventory existing wetland modelling methods and develop a catalogue of conceptual-numerical wetland modelling methods in hydrology based on wetland classifications and numerical water balance equations, 2) to implement conceptual-numerical wetland modelling methods in a regional hydrologic model case study and evaluate model performance to determine the impact of wetlands on simulation results, and 3) to examine how available wetland mapping products can inform wetland modelling.
A hydrologic model of the Nipissing watershed in Ontario was built using the Raven Hydrologic Framework and calibrated in a multi-objective calibration to both high and low flow objective functions in three modelling scenarios. The first modelling scenario (Scenario 1) contained no wetland representation; the second modelling scenario (Scenario 2) contained explicit wetland representation of one wetland conceptual-numerical model type; and the third modelling scenario contained explicit wetland representation of three wetland conceptual-numerical model types based on connectivity of wetlands to modelled streams and lakes.
Calibration results indicated good model performance for all model scenarios, as an adequate performance threshold of 0.50 for the Kling Gupta Efficiency (KGE) and log transformed Nash Sutcliffe Efficiency (logNSE) was achieved for both performance metrics. In calibration, Scenario 2 most often outperformed Scenario 1 (no wetland scenario) at individual calibration gauges and Scenario 3 (most complex wetland scenario) due to pareto solution uncertainty and site-specific properties.
Validation results indicated that Scenario 3 most often outperformed the other two scenarios across multiple performance metrics at individual flow gauges and handled low flows especially well when analyzing low flow performance metrics and hydrographs. This is attributed to Scenario 3 storing the most water in wetland depressions out of all modelling scenarios from abstraction, lateral diversion of water accounting for wetland contributing areas, and groundwater process parameters set up for each simulated wetland type. Percent bias median and spread across all flow gauges significantly decreased by 15% from Scenario 2 to Scenario 3, highlighting the importance of low flow accuracy to hydrologic model performance.
Flow duration curves and hydrographs plotted by flow gauge demonstrated that site-specific properties of the entire study area and individual gauge drainage areas can impact simulation results. There was no relationship found between gauge drainage area, wetland coverage percent by area, and model performance at individual gauges in this study.
Four wetland mapping datasets in Ontario were compared to select a wetland data input to the Nipissing model. By comparing each wetland dataset, a formalized checklist is provided for modellers to use as a reference when making similar comparisons between their own wetland mapping products. It is recommended that wetland mapping product comparisons for project suitability be performed by first comparing wetland coverage between datasets using the wetland polygon coverage by area, then comparing spatial variability between datasets by inspecting areas of overlap and non-overlap, and finally comparing data attributes, particularly wetland classifications and any discrepancies between dataset attributes.
While the results of this study demonstrate the importance of low flow accuracy to model performance through the representation of wetlands, improvements could be made to aid future studies. It is recommended that future studies select a watershed with high quality flow and meteorological data, basins with varying wetland coverage, and little to no water regulation influence (e.g., hydroelectric dams). It is also recommended that the wetland conceptual-numerical models presented in this thesis be further tested on watersheds of different sizes, different combinations of wetland types, and varying degrees of complexity
Integration of Borehole Geophysical Logging with Hydraulic Tomography Analysis for Improved Groundwater Flow and Transport Predictions
No full textHydraulic Tomography (HT) has been demonstrated to be a robust approach to characterize the subsurface heterogeneity, which employs the inversion of head data collected from multiple pumping tests to estimate the spatial distribution of hydraulic properties (e.g., hydraulic conductivity (K) and specific storage (Ss)). However, the resolution of K and Ss tomograms is reduced when the number of pumping tests and observation density gradually decrease. Another issue is that HT’s ability to predict solute/heat tracer transport behavior has not been rigorously examined for complex aquifer systems. Previous studies showed that different types of data (e.g., geological, geophysical, and other hydraulic testing data) carrying non-redundant information can be integrated with HT analysis to improve the mapping of K heterogeneity. This thesis evaluates the effectiveness of integrating geophysical logging data with HT analysis for improved imaging of K distributions. Furthermore, a heat tracer test was conducted in a highly heterogeneous glaciofluvial deposit to investigate the feasibility of reproducing the spatial distribution of observed temperature responses based on a heat transport model with HT K estimates.
Five sequential studies are documented in this thesis to explore the integration of borehole geophysical logging with HT analysis for improved mapping of K and porosity heterogeneity, which enables enhanced predictions of groundwater flow and solute/heat tracer transport:
(1) Study I integrated two conventional geophysical logging surveys, including electrical conductivity (EC) and gamma ray (GR) logging, with HT analysis to yield 2D K fields in a numerical sandbox experiment. A new spatial conditioning term was proposed to better delineate the hydrostratigraphy from geophysical logging data, which was used to derive the initial guess of K fields for geostatistical inverse modelling of HT analysis. The HT K models with comparative initial guesses of K distributions were evaluated based on their predictive capabilities for groundwater flow and solute transport.
After demonstrating the effectiveness of integrating geophysical logging with HT analysis for improved K estimation in a numerical sandbox study, the subsequent studies were conducted at the North Campus Research Site (NCRS) underlain by a highly heterogeneous glaciofluvial deposit.
(2) Study II conducted nuclear magnetic resonance (NMR) logging at the NCRS. Compared to conventional geophysical logging surveys, NMR logging can directly provide K estimates, as well as total porosity and effective porosity measurements. The petrophysical relationship between NMR signals and K was site-specifically optimized, and the NMR-derived downhole K profiles were compared with a variety of hydraulic measurements to evaluate their accuracy and resolution along boreholes.
(3) Study III constructed 3D K fields based on downhole NMR K profiles and evaluated the representativeness of these K models. Various spatial interpolation approaches were employed to generate spatial K patterns. A multi-level heterogeneity characterization approach was proposed to better represent the layered porous medium at the NCRS. The model performance to predict groundwater flow was examined through simulating the observed drawdown responses from multiple pumping tests.
(4) Study IV integrated NMR logging with HT analysis for improved characterization of subsurface heterogeneity. To highlight the importance of incorporating high-resolution initial K distributions to reduce the smoothness of K tomograms, a limited HT calibration dataset with fewer pumping tests and decreased observation density was utilized for model calibration. The effectiveness of this integration was evaluated through a comparative case study using varying numbers of head data for calibration and different spatial interpolation techniques for constructing initial NMR K models.
(5) Study V conducted a heat tracer test at the NCRS, in which a dense monitoring network was installed to record temperature responses. The ability of various characterization approaches (e.g., HT analysis) to accurately map K heterogeneity was investigated by reproducing the complex spatial distribution of the temperature breakthrough curves (BTCs). Additionally, NMR-derived effective porosity was used to map a heterogeneous porosity field. Lastly, the sensitivities of heat tracer plume migration to flow, transport, and thermal parameters were investigated.
The main contributions of these studies are: (1) conventional geophysical logging survey can provide hydrostratigraphic information to improve the resolution and accuracy of HT estimates; (2) NMR logging yields reliable downhole K estimates for interbedded layers of gravel, sand, silt and clay; (3) after spatial interpolation, 3D K models can be constructed based on NMR logging, which offers reasonable drawdown predictions to pumping tests; (4) integrating NMR logging with HT analysis can provide more representative K estimates consistent with the depositional environment, and the integrated models can still yield reliable K estimates at high resolution when only a limited head data is available for calibration; and (5) the complex temperature response from a heat tracer test can be best reproduced using HT analysis and NMR logging to represent the heterogeneous K and effective porosity fields. Based on their robust performance in predicting groundwater flow and solute/heat transport at different scales, this work advocates the joint use of HT analysis and borehole geophysical logging to characterize subsurface heterogeneity
Identifying the Minimum Number of Subadult Individuals in Grave 6 of the Wadi Faynan 100 Cemetery, Jordan
No full textEstimating the Minimum Number of Individuals (MNI) in commingled and fragmentary assemblages is particularly difficult for subadults, whose remains are often fragile and incomplete. This thesis applies a modified Landmark System to the subadult assemblage from Grave 6 at Wadi Faynan 100, an Early Bronze Age IB site in southern Jordan, to subadult MNI estimation. The analysis identified a minimum of 22 subadults, ranging from perinatal to late adolescence. These findings demonstrate the value of the Landmark System for subadult remains, expanding the methodological toolkit available for osteologists working with highly fragmentary collections. Beyond methodology, this study contributes to understanding Early Bronze Age mortuary practice in the southern Levant
Evaluating Window View Quality of Building-Integrated Photovoltaic Using Immersive Virtual Reality
No full textBuilding-Integrated Photovoltaic (BIPV) windows have gained increasing attention due to their onsite energy generating capability and possibility of reducing operational carbon, but their impact on occupants’ view experience in terms of view clarity and privacy has not been addressed. This research addresses this gap by assessing how various design approaches of mono- and poly-crystalline Silicon BIPV windows impact occupant satisfaction using a human-centric perspective, based on the combination of subjective experience and objective BIPV windows performance analysis.
For this purpose, an immersive virtual reality (IVR) test was developed which enabled more than 70 participants to assess BIPV window views in a virtual office setting. The IVR approach is a scalable process for early-stage assessment of the façade which is particularly useful in environments where the development of a physical prototyping is expensive, time-consuming or not possible.
Solar cell size and visible light transmittance (TVIS) were varied to assess trade-offs among view clarity, privacy, and overall satisfaction. The selected by the participants configurations were further analyzed in terms of annual daylighting performance and PV energy generation. This included performance metrics as daylight autonomy and glare probability, as well as potential generated electricity (kwh/m2/year), to paint a picture of how user-preferred BIPV configurations will perform in the context of energy generation and daylighting.
The results show a trend where BIPV configurations with increasing TVIS were preferred overall by participants for better view clarity and higher overall satisfaction, even if their view privacy was slightly compromised. A BIPV window with full size cells and TVIS = 0.48 was found to be the preferential configuration, providing both view satisfaction and an acceptable PV output.
This scalable, repeatable, and cost-effective approach enables researchers and designers to simulate and assess occupant perception of BIPV window views, offering valuable insights for sustainable, occupant-centered façade design. These insights aim to guide improvements in BIPV window design, ensuring a balance between maximizing energy generation while maintaining window functionality and occupant comfort
Exploring the Voice of OCD
No full textCritical inner dialogues are prevalent and clinically significant features of depression and eating disorders. According to the Interpersonal Circumplex (IPC), qualities of a communication can be rated on orthogonal dimensions of tone (hostile to warm) and authority (dominant to submissive), wherein qualities of a communication elicit complementary responses, with warmth eliciting warmth and dominance eliciting submission. Preliminary research by Chiang and Purdon (2020) found that obsessions are often experienced as a neutral dominant voice, however, this is the only study that has investigated the tonal qualities of obsessions. The current study is a replication and extension of these preliminary findings, exploring the phenomenology of the OCD voice and its association with OCD symptom severity and insecure attachment. Adults with a past diagnosis of OCD (N=20) were administered a semi-structured interview developed for this study. The interview included two within-participants conditions; one in which participants were asked about obsessions that evoked a compulsion and another in which the obsession did not evoke a compulsion. Well-validated measures were used to assess appraisals of the OCD voice, OCD symptoms, and attachment style. Qualitative results showed that all participants reported experiencing an internal OCD voice, and the majority (85%) engaged with it in internal dialogue. The OCD voice was predominantly rated as neutral and dominant across both obsessive-compulsive episodes. Quantitative analyses revealed that greater perceived benevolence and omnipotence of the OCD voice significantly predicted more severe OCD symptoms. These findings support the prevalence of a neutral and dominant OCD voice among a sample of adults with a past diagnosis of OCD. Appraisals of the OCD voice, particularly benevolence and omnipotence, may contribute to symptom severity. This study highlights the potential therapeutic value of targeting individuals’ relationship with the OCD voice
Multi-Outcome Trajectories in Traumatic Brain Injury
No full textTraumatic Brain Injury (TBI) presents a global health challenge, affecting millions of individuals annually, resulting in diverse outcome trajectories that complicate patient management. The heterogeneity in TBI outcomes, influenced by varied clinical presentations and injury responses, requires advanced analytical approaches. The analysis of trajectories using single metrics, such as the Glasgow Outcome Scale Extended Global (GOSE), falls short of capturing the multi-faceted nature of TBI progression, often overlooking the complexity of individual patient experiences.
This thesis reports on two studies. First, a systematic scoping review was conducted to synthesize the current research on trajectory analysis in TBI, followed by a modeling study. This work identifies 6 distinct multi-outcome trajectories in TBI patients by employing Latent Class Mixed Models (LCMM) and clustering approaches. Utilizing longitudinal data from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury study (TRACK-TBI), a prospective multicenter observational cohort study conducted at 18 level 1 trauma centers across the United States, which includes 17 selected outcome measures collected at four time points post-injury, provides a comprehensive understanding of the heterogeneous progression of TBI. By addressing the limitations of single outcome analyses, this research contributes to a better understanding of TBI progression that can lead to the optimization of TBI management and treatment. The future integration of these trajectories will facilitate the development of personalized treatment strategies, ultimately improving patients’ recovery
Transforming General Aviation Pilot Training: Integrating Sustainability, Human‑Centered Evaluation, and Immersive Digital Innovation
No full textThe general aviation (GA) sector plays a foundational role in training future pilots but faces increasing pressures to modernize amid growing demands for environmental responsibility, instructional effectiveness, and technological adaptation. This dissertation investigates how early-stage GA pilot training, specifically ab initio training, can evolve to meet these imperatives through a multidimensional approach spanning sustainability analysis, learner and practitioner evaluations, and technology integration studies.
Six studies form the core of this work. The first study quantifies the environmental impact of GA flight training in Canada, estimating approximately 30,000 tonnes of annual CO₂ emissions and highlighting opportunities for emissions reduction through greater integration of simulation-based training, while considering additional sustainable strategies. The second study examines student and licensed pilots’ perceptions of Canada’s GA licensing practices, revealing concerns about the adequacy of flight-hour accumulation as a proxy for competence, alongside broader systemic shortcomings related to outdated instructional methods, inconsistencies in performance assessment, and limited integration of modern technologies—together supporting calls for competency-based reforms. The third study evaluates technology acceptance among student and licensed pilots, identifying cautious receptivity toward assistive training technologies intended to supplement, rather than replace, in-aircraft instruction—albeit tempered by concerns around regulatory inertia, cost barriers, and cultural conservatism. Building on this foundation, Studies 4 and 5 empirically validate AR’s instructional utility in high-fidelity simulator and real-aircraft environments, demonstrating enhanced procedural learning and cognitive engagement but also surfacing usability and ergonomic challenges. Study 6 captures Certified Flight Instructors’ (CFIs) perspectives on AR integration, revealing both pedagogical potential and pragmatic concerns requiring institutional support.
Synthesizing these findings, the dissertation provides an evidence-based foundation for making GA pilot training more sustainable, competency-centered, and technologically progressive. The work contributes novel empirical insights into an underexplored sector of aviation research, offers actionable strategies for training reform, and highlights the potential for CO₂ reductions by substituting in-aircraft training hours with FSTD- and AR-supported instruction. Ultimately, it advances a vision of GA education that is safer, more effective, and aligned with sustainability goals essential to the future of civil aviation
Turbulence Closure Modeling Using Kolmogorov-Arnold Networks and Bayesian Optimization
No full textThis thesis presents two complementary approaches to improving Reynolds-averaged Navier–Stokes (RANS) turbulence modeling through machine learning and optimization. First, we introduce a realizability-informed framework for training stable and physically consistent machine learning-based anisotropy closures within the tensor basis neural network (TBNN) paradigm. We develop a physics-based loss function that penalizes non-realizable predictions during training, enhancing model stability and generalization. To reduce model complexity and improve interpretability, we replace conventional multilayer perceptrons (MLPs) with Kolmogorov–Arnold Networks (KANs), forming the Tensor Basis KAN (TBKAN) architecture. The TBKAN framework is evaluated across three canonical flows, flat plate, periodic hills, and square duct, demonstrating improved prediction fidelity, stability, and realizability compared to baseline TBNNs and traditional eddy-viscosity models.
Second, we explore the application of Bayesian optimization for data-driven calibration of RANS model coefficients, focusing on the generalized k–omega (GEKO) model. The proposed turbo-RANS framework is applied to a converging-diverging channel flow case characterized by adverse pressure gradients and separation. Optimized coefficients yield improved predictions of wall-bounded quantities and streamwise velocity profiles when compared against direct numerical simulation (DNS) and large eddy simulation (LES) references.
Together, these contributions address key limitations in existing data-driven turbulence modeling approaches, namely, lack of physical realizability, interpretability, and predictive robustness, while providing practical tools for improved RANS performance in engineering flows. All code and models developed in this work are made publicly available to encourage further research and adoption
Ontario Climate Risk: Workshop Report
No full textDespite having the country's largest economy, population, and number of universities with world-class expertise on the topic, Ontario lacks a hub for sharing information and best practices, and fostering connections between those working to address climate risk. There is a need for rigorous inquiry into localized climate impacts, including the potential for increased frequency and intensity of heatwaves, disruptions in water availability, and impacts on the Great Lakes region ecosystems. The significant expertise amongst academics and other researchers across the region regarding the complex dynamics between these factors will be necessary for devising effective and equitable mitigation and adaptation strategies. Identifying and addressing these gaps in our knowledge is paramount for developing region-specific strategies to mitigate and adapt to climate change, thereby contributing to the overall resilience and sustainability of Ontario's communities.
In this context, the Ontario Climate Risk Workshop, held on October 30-31, 2024, brought together participants from academia, public and private sectors, non-governmental organizations, Indigenous leaders, elected officials, and representatives of the general public to share knowledge, discuss existing initiatives, and co-create a research agenda for addressing climate risk in the province. The event was structured around eight thematic sessions, each of which is documented in this report. Within each session, participants examined and discussed existing resources and barriers relevant to addressing climate risks associated with the respective theme.
These proceedings provide an overview of the discussions for each session and were co-developed by our research team along with the respective session leads.Social Sciences and Humanities Research Council of Canada, Grant 611-2023-0698