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Dynamic stability of pile foundations under seismic excitations with two frequencies
Get PDFIn civil engineering, deep foundation systems, specifically pile foundations, play a critical
role in transferring the structural loads of heavy constructions from superstructures to deeper
layers of soil. The consequences of such failures are far-reaching and can incur property damage,
structural failure, and tragically, loss of human lives. It is imperative to address the potential
risks associated with pile foundation failure, particularly under seismic conditions.
Conventionally only dynamic forces with a single frequency are investigated. In many cases of
Civil Engineering, however, the dynamic force is not periodic with a single frequency but quasiperiodic with multiple frequencies. While there have been numerous studies on the buckling
stability of piles, there is a noticeable scarcity of research that considers the influence of seismic
excitations with two frequencies.
The primary objective of this research is to study the dynamic stability of pile foundations
under seismic excitations with two frequencies analytically and numerically. The study
commences by driving the equation of motion for a pile foundation under earthquake, which is
decoupled into an ordinary differential equation with variable coefficients of two frequencies.
The harmonic balance method is used to analytically construct the stability diagrams of the pile.
A numerical method is also presented to study the stability of columns under dynamic loads with
two frequencies. The numerical results of instability diagrams can also serve as a calibration of
other approximate results. As an application example, the dynamic stability of a real pile
foundation is investigated using both the harmonic balance method and the numerical method.
This is followed by parametric studies involving factors such as elastic foundation rigidity,
damping, and dynamic and static loads on the instability regions. The outcomes of this research
carry significant practical implications, particularly in the domain of designing pile foundations
for mega-structures. Designers can leverage the findings of this study to incorporate the effects
of multiple frequencies on pile behavior into their design considerations, thereby enhancing the
structural age and safety of constructions
Seasonal streamflow drought forecasting based on pattern recognition concepts using statistical and machine learning approaches
Get PDFUnderstanding and forecasting drought events is crucial for effective water resource management
and mitigation planning. Forecasting droughts is challenging due to their inherently complex
patterns and dependencies. However, there is a tendency for droughts to occur during specific
seasons or times of the year and exhibit distinct seasonal variability. This research focuses on
analyzing seasonal drought patterns using a grouped data concept, where similar data points are
aggregated into groups to represent distinct hydrological drought conditions.
The objective is to develop a methodology that can effectively recognize and predict droughts
based on these grouped streamflow data sets. In the proposed study exploratory data analysis
techniques are used to recognize the seasonal patterns within the data to extract meaningful drought
patterns from the streamflow data. The study employed a combination of statistical methods and
machine learning techniques, including Markov models and Long Short-Term Memory models
(LSTM), to forecast the grouped seasonal streamflow data. A Markov model is employed to model
the transition probabilities among hydrological drought states, capturing the temporal
dependencies in streamflow behaviour. Subsequently, a Hidden Markov model (HMM) is utilized
to employ the underlying states (or underlying drought levels) in observed streamflow data. To
further enhance forecasting capabilities, monthly and weekly LSTM networks are utilized to learn
long-term sequential dependencies and forecast future streamflow drought patterns.
The study area was selected as the Palliser Triangle, the driest region in Canada. A total of 25 river
stations (catchment area ranging from 319 to 47,800 km2
) were chosen, representing a range of
river capacities: low flow (annual runoff range from 0 to 50 mm), medium flow ((annual runoff
range from 50 to 175 mm), and high flow (annual runoff more than 175 mm) The monthly flow
sequences of these rivers displayed the coefficient of variation ranging from 0.61 to 3.84, skewness
from 0.57 to 8.39 and lag-1 autocorrelation from 0.2 to 0.63. In view of the highly skewed nature
of monthly flows, the Box-Cox transformation was applied to normalize the data sequences and
the normalization parameter ƛ ranged from -0.96 to 0.16. The Box-Cox transformation proved
powerful for the normalization of flow data sets, which provided a strong platform for the analysis
and forecasting of hydrologic droughts. The model results revealed that the discrete Markov model
performed best for medium-flow rivers, achieving an average forecast accuracy of 65%, and the
Hidden Markov model demonstrated superior performance for both low-flow and high-flow rivers,
with an average forecast accuracy of 74%. The LSTM model showed consistent performance
across all river types, providing monthly forecasts with approximately 80% accuracy and weekly
forecasts with an impressive 90% average accuracy. [...
The medical applications of hyperpolarized Xe and nonproton magnetic resonance imaging
Get PDFHyperpolarized 129Xe (HP 129Xe) magnetic resonance imaging (MRI) is a relatively young
field which is experiencing significant advancements each year. Conventional proton MRI is
widely used in clinical practice as an anatomical medical imaging due to its superb soft tissue
contrast. HP 129Xe MRI, on the other hand, may provide valuable information about internal organs
functions and structure. HP 129Xe MRI has been recently clinically approved for lung imaging in
the United Kingdom and the United States. It allows quantitative assessment of the lung function
in addition to structural imaging. HP 129Xe has unique properties of anaesthetic, and may transfer
to the blood stream and be further carried to the highly perfused organs. This gives the opportunity
to assess brain perfusion with HP 129Xe and perform molecular imaging. However, the further
progression of the HP 129Xe utilization for brain perfusion quantification and molecular imaging
implementation is limited by the absence of certain crucial milestones.
This thesis focused on providing important stepping stones for the further development of
HP 129Xe molecular imaging and brain imaging. The effect of glycation on the spectroscopic
characteristics of HP 129Xe was studied in whole sheep blood with magnetic resonance
spectroscopy. An additional peak of HP 129Xe bound to glycated hemoglobin was observed. This
finding should be implemented in the spectroscopic HP 129Xe studies in patients with diabetes. [...
Artificial intelligence-enabled recommendation system for electric vehicles
Get PDFThe drastic growth in the conventional transportation system raises serious air pollution concerns. Eco-friendly vehicles, in contrast, have been introduced as an alternative
to alleviate such environmental issues. To support the Canadian government’s goal of
achieving 100% sales of zero-emission vehicles by 2035, there is an increasing need for
advancements in charging infrastructure and the performance of Electric Vehicles (EVs).
These improvements aim to address range anxiety which is the primary concern of EV
consumers who fear running out of electricity during a journey and being unable to find
a charging point. However, so far, the main investment focus has been on the installation
of Fixed Charging Stations (FCSs) which requires significant budget contributions and
proper charging station placements. Therefore, to achieve higher EV popularity, this work
aims to elevate user satisfaction and alleviate Range Anxiety by developing an intelligent
system to manage EV charging demands, accurately estimating State of Charge (SoC) levels, and offering user-centric suitable service recommendations. Nevertheless, the scarcity
of EVs historical data for Artificial Intelligence (AI)-based predictions poses a significant
difficulty. To mitigate the aforementioned concern, we present a model based on Deep
Transfer Learning (DTL) between domain-variant data sets, to reduce the need for the
existence of a vast amount of EV data, including driving characteristics and patterns. [...
Integration and experiences of immigrants within Canada
Get PDFThis portfolio was created with the goal of better understanding and closely
examining the various difficulties and barriers that newly landed immigrants face upon
entering Canadian society. Immigrants make up a large portion of Canadian society, and yet
many changes need to be made within federal and provincial policies to create a more
equitable living experience for them. This portfolio is composed of three different parts: a
literature review, chapter book (with AI generated images), and a diverse literature guide. The
AI generated images were a choice that was not taken lightly, as I found struggle in creating
images using digital art softwares. My experience as a Syrian-Canadian, the daughter of
Syrian immigrants, and the relative of Syrian refugees, has influenced the creation of the
chapter book and diverse literature guide, as they mirror my own childhood desires and
experiences. I hope to have my diverse literature guide used throughout schools in Canada as
a basis for teachers to reach for when they are unsure of how to include positive media
representation of diverse students within their classroom. Furthermore, I hope to have my
chapter book published, so that the many children within Canada who come here not
knowing much English, can know that their experiences are valid. And even better, so that
their classmates who do know English are able to be more empathetic and understanding of
what it is like not to speak the main language of a country
Privacy-preserving EEG data frameworks for brain-computer interfaces
No full textNon-invasive brain-computer interface (BCI) systems rely on brainwave activity, predomi-
nantly captured through Electroencephalography (EEG), to facilitate seamless interactions
with digital platforms. Throughout its development, EEG-driven BCIs have touched in-
dustries as diverse as entertainment, healthcare, and cybersecurity. However, despite im-
provements in functionality and accuracy, the critical issue of securing the vast amounts of
sensitive EEG data collected by these systems has remained largely overlooked, posing sig-
nificant privacy risks. While techniques like data anonymization, encryption, masking, and
perturbation aim to protect privacy, they often degrade the quality of the data and fail to
fully eliminate the risk of re-identification. In response, we have developed multiple privacy-
preserving frameworks: a quantum-inspired Differential Privacy-based generative model, a
R ́enyi Differential Privacy (RDP) based Federated model, and a privacy-adaptive Federated
Split Learning framework, featuring Secure Aggregation and Autoencoders. Each framework
is designed to generate synthetic EEG data that comply with privacy protection standards
while ensuring robust data utility for downstream analysis. Modern defenses that focus on
privacy frequently sacrifice performance or depend on large amounts of external data, which
can limit their practicality. Our approach not only mitigates these limitations, but also
significantly strengthens defenses against membership inference and reconstruction threats
Sustainable forest management: the potential impacts of underutilization in Ontario Crown forests
Get PDFOntario has in recent history harvested a volume in cubic meters less than the
volume available to harvest as dictated by the Average Allowable Cut (AAC). While
harvesting more than the AAC dictates is unsustainable and will lead to mature wood
supply shortages, this study aims to analyze and discuss the impacts of the current rate
of underutilization in Ontario’s forests, whether positive or negative. A literature review
was conducted, and the following were identified as components potentially impacted by
underutilization: water quality, old growth area, mixedwood biodiversity, fire risk,
economic consequences, volume, and future landscape goals. A case study was
conducted on two forests experiencing underutilization: the Algoma Forest and the
Kenogami Forest. It was found that water quality, old growth area, and mixedwood
biodiversity are potentially positively impacted by the current rates of utilization in
Ontario. However, there are negative implications for volume, economies associated
with the forest, and in the ability to meet future landscape targets and long-term
management directions (LTMD’s). It is inconclusive whether there is an impact to fire
risk associated with underutilization.
Further studies are needed to completely understand the impacts that the current
harvesting rates are having on the landscape to inform Ontario Forest managers and
acquire a better understanding of anthropogenic impact, or lack thereof, on the
landscape
Variations of saproxylic beetle assemblages within the same white spruce logs across early and advanced decay classes
Get PDFSaproxylic species play a multitude of essential ecological roles within the forest
ecosystem by undergoing a distinct succession as deadwood decays in the early to late
successional stages. As of 2024, knowledge regarding saproxylic beetle community drivers, in
terms of biotic interactions and larval niches, is still minimal. Striving to understand the
structure, function, drivers of community assembly, and spatiotemporal dynamics of saproxylic
fauna in forest ecosystems is necessary to ensure the conservation of saproxylic biodiversity.
This study investigates whether there are patterns of diversity, abundance, and composition of
saproxylic beetles within sections of the same white spruce log and whether there are differences
or similarities between the early decay class (DC2) and advanced decay class (DC5) of white
spruce logs. The study was carried out in a 10-ha non-harvested white spruce [(Picea glauca
(Moench) Voss)] stand (56°79′N, 118°36′W, 758 m a.s.l.) located at the Ecosystem Management
Emulating Natural Disturbances (EMEND) research site in northwestern Alberta. Both DC2 and
DC5 white spruce logs were cut into five bolts, 60 cm long, with 60 cm intervals between each
bolt. A total of 30 white spruce bolts were transported to Berlese funnels where saproxylic
beetles were collected, and later identified to species and feeding guild. The data collected was
analyzed using Excel and RStudio, using Generalized Linear Model (GLM) to compare species
richness and abundance and Non-metric Multidimensional Scaling (NMS) ordination to
understand community structure. Results from this study indicated that mean species richness did
not differ significantly within and between decay classes. However, saproxylic beetle abundance
was significantly highest in bolt two, second from the stump, and gradually declined in
abundance towards bolts situated higher in the tree. Even though species richness did not differ
significantly within and between decay classes, DC2 showed less similarity in species
composition across the log replicates than DC5. These results indicate that saproxylic beetle
assemblages are spatially aggregated within the same decay class of log. This study revealed that
log sections closest to the stump are recommended to be left post-harvest to aid saproxylic beetle
population persistence rather than leaving treetops. Most importantly, understanding the niche
partitioning of saproxylic species at different decay stages of deadwood, in terms of competition
and co-existence, can contribute to developing forest management strategies that have the least
impact on saproxylic beetle populations
A comparative study of regional and cover type influences on carbon content in above-ground woody live biomass
Get PDFThis thesis provides an analysis of the influence of forest region, cover type, and species
composition on carbon storage in above-ground woody biomass across boreal and Great
Lakes-St. Lawrence (GLSL) regions. The plot data was gathered for Perimeter Forest
Ltd., which specialises in providing high-integrity carbon credits resulting from its forest
management and biodiversity conservation efforts in Canada. The study unveils
significant regional differences in carbon storage capabilities, with the GLSL region's
hardwood ecosystems exhibiting superior carbon storage potential compared to the
boreal region. The study also found that the type of cover type had an influence on the
amount of carbon present. Tolerant hardwoods showed higher levels of carbon in the
Great Lakes-St. Lawrence region, while mixed woods showed higher levels in the boreal
region. These differences are attributed to the distinct ecological adaptations, growth
rates, and sizes of species within each region. The findings highlight the necessity of
sophisticated, dynamic management approaches that consider regional differences,
species diversity, and stand ecological characteristics to maximize carbon storage and
contribute significantly to climate change mitigation efforts
Structural layout optimization framework of tall buildings subjected to wind load
No full textConventional design methodology for tall buildings is a time-consuming and repetitive trial-anderror procedure with a limited probability of yielding an optimal solution that satisfies
architectural, structural and serviceability requirements. Tall buildings are typically slender
structures and mainly depend on a Main Wind Force Resisting System (MWFRS) (e.g., shear
walls, cores, and bracing systems) to withstand the lateral load of wind events, where a minor
change in their layout, size, or shape will affect the cost tremendously. Consequently, a structural
layout optimization procedure will result in a more economical and sustainable design. Most
previous studies focused on developing optimization frameworks and algorithms that rely on using
static wind loads. Even with the adoption of dynamic wind load, the focus was on the vertical
layout of the lateral load-resisting systems in a simplified form and as a single objective
optimization due to the demanding computational costs. Therefore, the first and main objective of
this research is to develop a novel structure-wind optimization framework (SWOF) to find the
optimal horizontal (e.g., shear wall) layout of tall buildings subjected to wind loads. SWOF is
considered a genetic algorithm-based framework that uses a data-driven surrogate model to
evaluate its constraints and objective functions. These surrogate models rely on a training dataset
prepared using the Finite Element Method (FEM), which has been created using an open
application program interface (OAPI) MATLAB code. [...