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Senast inlagda
Deep learning-based methods for segmentation and labelling of clay
(2025) Örtendahl, Theo
Recent advancements in x-ray technology have enabled non destructive 3D sub-micron
imaging of clay. In this work, a 3D tomography of kaolinite particles is analysed.
Conventional segmentation algorithms are used along with a deep learning-aided
method, which brings novelty to the clay research area. The clay image is segmented
to acquire morphological properties of the material, intended to be used to inform
continuum models for clay used at the engineering scale. Imaging clay is especially
challenging because of its small particle size and thin aggregated platelets. A small
clay dataset will be developed to evaluate the performance of segmentation techniques
and to train a machine learning-model called the Segment Anything Model 2 (SAM
2). Advanced contemporary studies in biomedical segmentation show compelling
results using SAM 2 and this study is proposing to bring this technique into the area
of geomechanics. This study aims to lay a path for future research to strengthen
the link between physical relationships and observed clay behaviour by providing
information of clay micro-structures.
Analysis of historical quay wall structures - A Sensitivity Study to Develop a Method of Analysing Existing Structures
(2025) Stridh, Tobias; Arriola Meza, Juan José
This thesis investigates methods for assessing the structural capacity of historic quay walls
in central Gothenburg, many of which are in need of renovation. A theoretical study of typ ical failure mechanisms was carried out, and analysis methods previously applied to similar
quay walls in Amsterdam were studied to create a finite element (FE) model in Abaqus. This
model was validated against existing Dutch models. Additionally, a reduction model was
developed in Grasshopper to account for geometric constraints on soil capacity. Parametric
studies were conducted to evaluate key factors influencing structural integrity, resulting in a
practical tool for assessing historical quay wall structures.
For validation, the developed model was compared with results from the Dutch case. The
simulations included various combinations: assessing the impact of the reduction factor,
comparing drained and undrained conditions in the wedge model, and evaluating the results
against hand calculations.
In the Gothenburg case study, two models were developed to analyse the effect of differ ent structural layouts. The FE modelling considered different conditions, resulting in 96
simulations providing data to conduct a sensitivity study. Approximately 10 % of these
combinations were found to be in a critical state.
The study identified that the front pile and pile diameter, in combination with at-rest earth
pressure, where the most critical cases. The status of the filling, in the top soil layers beneath
the timber deck, was the most critical condition to determine. These requires consideration
when assessing the structural integrity of historical quay walls.
It was proven that a parametric finite element model, implementing a soil wedge reduction
model with visual programming, can be used to conduct a sensitivity study on historical
quay walls.
User willingness to accept vehicle-to-grid
(2025) Santiago Arana Torrealba, Ivan
As electric vehicles (EVs) become more prevalent and the energy mix increasingly in cludes variable renewable energy (VRE), there is a growing need for strategies that bal ance electricity supply and demand. Vehicle-to-grid (V2G) technology allows EVs to act
as distributed storage units, feeding energy back to the grid during peak hours. However,
the effectiveness of this approach depends on user participation. This thesis explores
the willingness of EV users in Gothenburg, Sweden, to engage in V2G programs and
accept different charging conditions and remuneration. A stated choice experiment was
designed and distributed to EV users and vehicle owners, collecting 153 valid responses.
The survey included demographic questions, behavioral preferences, concerns, and dis crete choice tasks involving four key attributes: minimum guaranteed range, minimum
plug-in hours, days of required connection per week, and monthly remuneration. The
resulting data were analyzed using a multinomial logit model implemented in Biogeme.
The findings reveal that plug-in time and financial compensation are the most influ ential factors affecting involvement. Longer plug-in requirements reduce utility, while
higher recompense increases it. Users also showed moderate sensitivity to guaranteed
driving range and a slight positive inclination toward routine schedules. Willingness to
accept calculations and scenario simulations show that participation likelihood increases
significantly when plug-in requirements are lower or when monthly payment reaches real istic upper thresholds. This research provides quantitative insights into user preferences
and offers recommendations for designing user-centric V2G programs that align technical
feasibility with behavioral acceptance, ultimately supporting grid flexibility and decar bonization goals.
Analysis on the determinants of EV purchase intention in Sweden
(2025) SHEN, XIAOZE; GAO, SHANG
This study uses a discrete choice experiment embedded in a survey to explore the
determinants of Swedish consumers’ choice between electric vehicles (EVs) and
internal combustion engine (ICE) vehicles. A total of 373 respondents, resulting in
7,266 valid choice observations, were collected and analyzed using binary logistic
regression models. Model specifications include both vehicle-specific attributes (e.g.,
price, range, maintenance costs, charging time, charging convenience, and emissions)
and demographic characteristics (e.g., age, gender, education, income, and family
structure).
The results show that economic and infrastructure considerations dominate consumers’
decision-making process. Specifically, vehicle price, maintenance costs, and the
availability of home charging infrastructure are significant attributes of EV adoption.
The existence of a home charger is a particularly important driver, increasing the
probability of choosing an EV by nearly 60 percentage points on average. In contrast,
attributes such as range and charging time, while in the direction of theoretical
expectations, are not statistically significant in the current sample. The probability
analysis also highlights that in the absence of home charging facilities, the impact of
price cuts is relatively limited, suggesting that policymakers should increase investment
in EV charging facilities.
The study provides practical insights for policymakers aiming to accelerate the adoption
of EVs in Sweden. In addition to targeted financial incentives, efforts should focus on
improving private and public charging infrastructure. The findings also contribute to a
broader understanding of how practical and infrastructure factors influence low-carbon
transport choices in European markets.
Impact of Road Work Zones on Traffic Flow and Safety - A VISSIM-Based Analysis of Driving Behavior and Risk Factors
(2025) MORIN, Elin; JOHANSSON, Elin
To achieve Vision Zero and eliminate all fatalities and severe injuries in road traffic, it is
necessary to improve road safety for both road users and road workers. Accidents and
incidents that occur in work zones could be prevented by following national regulations
and implementing measures such as putting up signs, barriers, and speed limits. Further,
the work zone safety is closely related to driving behavior.
The aim of this study is to examine how work zones affect traffic flow and road safety
with a focus on the Swedish driving behavior and national regulations. This study fills
a research gap addressing the lack of simulation studies on work zones in Sweden. A
literature study and interviews were conducted to present the regulations and understand
the current situation regarding road safety. A case study area was observed and recorded
in connection with a work zone. Machine learning was used to extract parameters
from the Swedish traffic flow, which was used to calibrate a simulation scenario that
correlated with a general Swedish work zone traffic flow. The model was improved
by changing parameters that mimic a lower speed limit in the work zone and driving
behavior with earlier merging.
It is found that Swedish drivers generally exhibit non-aggressive driving behaviors, in cluding gap acceptance, adherence to speed limits, and early merging. There is a vari ation of risks of work zone safety, where several situations are believed to occur due
to stressed drivers or a lack of information. Safety issues due to driving behavior were
tested in the traffic simulation tool VISSIM, where an improved design simulation sce nario illustrated a work zone where the speed limit was reduced and drivers merged
earlier compared to the calibrated and adjusted scenario. The improvements impacted
travel time by 2.2%, an insignificant increase compared to the enhanced safety to which
the lower speed contributes.