As ML (Machine Learning) and deep neural networks get more used in many systems,the need to understand and test such systems becomes more actual. When designing a newsystem that contains ML models, the safety of this system becomes inevitably important.This rises the need to discuss a strategy to deal with the potential problems and weak-nesses in such a system. This thesis provides findings from literature and illustrates thepotential strategies in the area of image recognition in a comprehensive way. Lastly, theresult presented in this thesis shows that using an ML component in a complex softwaresystem with high safety requirements requires adopting software methodologies, such asMLOps (Machine learning operations) to monitor such a system and give suggestions tohow to test and verify an ML model integrated into a larger software system.

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Highway bridges are among the most vulnerable and expensive components in transportation networks. In response, the Government of Ontario has allocated $26 billion in the next 10 years to address issues pertaining to aging bridge and deteriorating highway infrastructure in the province. Although several approaches have been developed to guide their rehabilitation, most bridge rehabilitation approaches are focused on the component level (individual bridge) in a relative isolation of other bridges in the network. The current study utilizes a complex network theoretic approach to quantify the topological characteristics of the Ontario Bridge Network (OBN) and subsequently evaluate the OBN robustness and vulnerability characteristics. These measures are then integrated in the development of a Multi Scale Bridge Classification (MSBC) approach—an innovative classification approach that links the OBN component level data (i.e., Bridge Condition Index and year of construction, etc.) to the corresponding dynamic network-level measures. The novel approach calls for a paradigm shift in the strategy governing classifying and prioritizing bridge rehabilitation projects based on bridge criticality within the entire network, rather than only the individual bridge’s structural conditions. The model was also used to identify the most critical bridges in the OBN under different disruptions to facilitate rapid implementation of the study results. / Thesis / Master of Applied Science (MASc)

Yes / Assuring safety and thereby certifying is a key challenge of many kinds of Machine Learning (ML) Models. ML is one of the most widely used technological solutions to automate complex tasks such as autonomous driving, traffic sign recognition, lane keep assist etc. The application of ML is making a significant contributions in the automotive industry, it introduces concerns related to the safety and security of these systems. ML models should be robust and reliable throughout and prove their trustworthiness in all use cases associated with vehicle operation. Proving confidence in the safety and security of ML-based systems and there by giving assurance to regulators, the certification authorities, and other stakeholders is an important task. This paper proposes a framework to handle uncertainties of ML model to improve the safety level and thereby certify the ML Models in the automotive industry.

Design optimality plays a central role in the area of statistical experimental design. In general, problems in design optimality are composed of two vital, but separable, components. One of these is determining conditions under which a design is optimal (such as criterion bounds, values of design parameters, or special structure in the information matrix). The other is construction of designs satisfying those conditions. Most papers deal with either optimality conditions, or design construction in accordance with desired combinatorial properties, but not both. This dissertation determines optimal designs for three treatments in the one-way and multi-way heterogeneity settings, first proving optimality through a series of bounding arguments, then applying combinatorial techniques for their construction. Among the results established are optimality with respect to the well known E and A criteria. A- and E-optimal block designs and row-column designs with three treatments are found, for any parameter set. E-optimal hyperrectangles with three treatments are also found, for any parameter set. Systems of distinct representatives theory is used for the construction of optimal designs. Efficiencies relative to optimal criterion values are used to determine robustness of block designs against loss of a small number of blocks. Nonisomorphic bal anced incomplete block designs are ranked based on their robustness. A complete list of most robust BIBDs for v ≤ 10, r ≤ 15 is compiled. / Ph. D.

No / This paper describes the experimental results of ten fire tests on medium-scale restrained steel sub-frames to investigate the relative behaviour and robustness of different types of steel joint in steel framed structures in fire. The ten fire tests were designed to investigate the effects of two column sizes (simulating two different levels of axial restraint to the connected beam) and five different types of joint, including fin plate, web cleat, flush endplate, flexible endplate and extended endplate connections. Each test frame, in the form of “rugby goalpost” consisting of one beam and two columns, was connected through two identical beam to column joints. All the steelwork was unprotected except for the top flange of the beam which was protected to simulate the effect of a concrete slab in reducing the beam top flange temperature. The column ends were restrained to examine the effects of axial restraint on the beam and the joints. This paper presents the observations of structural fire behaviour, including joint failure modes and beam limiting temperatures, the development of deflections at beam middle span and axial forces in the joints at elevated temperatures. The main conclusions are: (1) failure (fracture) was observed only in joints when the beam was in catenary action and a variety of joint failure modes were observed which provides valuable data in understanding joint behaviour; (2) the medium-scale steel beams were able to undergo very large deflections View the MathML source without failure; (3) the specimens with stronger connections such as extended endplate reached higher than their limiting temperatures, defined as the beam bottom flange temperature at middle span at which the axial load in the beam returned to zero. But the difference in beam limiting temperatures using different types of joint is small, less than 50 °C; also the column size had little effect (less than 30 °C) on the beam limiting temperature; (4) the beams connected to the larger column experienced less deflections, but higher axial force due to the higher axial restraint to the beam, which led to fracture of the joint components in these tests; in contrast, the lighter columns visibly deformed and formed plastic hinges at the joints, but there was little evidence of connection fracture in the test frames using the light columns; (5) the web cleat connection appears to have the best performance.

Chronic pain is a pervasive health issue, affecting a significant portion of the population and posing complex challenges due to its diverse etiology and individualized impact. To address this complexity, there is a growing interest in grouping chronic pain patients based on their unique treatment needs. While various methodologies for patient grouping have emerged, leveraging graph-based approaches to produce and evaluate such groupings remains largely unexplored. Recent studies have shown promise in integrating knowledge graphs into exploring patient similarity across different biological domains, indicating potential avenues for research. Additionally, there is a growing interest in investigating patient similarity networks, highlighting the importance of innovative approaches to understanding chronic pain. Graphs offer a transparent and easily interpretable framework for analyzing patient classifications, providing valuable insights into underlying patterns and connections. By leveraging graph theory, this thesis proposes a novel approach to address the terminological disparities that exist across disciplines studying chronic pain. By constructing a graph of pain-related terminology sourced from interdisciplinary literature, we aim to facilitate link prediction and clarify connections among disparate terminologies. This approach seeks to bridge disciplinary divides, fostering a cohesive understanding of chronic pain and promoting collaborative efforts toward effective management and treatment strategies. Through the integration of graph theory and interdisciplinary research, this thesis contributes to advancing our understanding of chronic pain and lays the groundwork for future explorations in patient grouping and treatment optimization by proposing a graph-based clustering method as well as a method for evaluating the robustness of a cluster.

Yes / Machine learning (ML) is increasingly employed for automating complex tasks, specifically in autonomous driving. While ML applications bring us closer to fully autonomous systems, they simultaneously introduce security and safety risks specific to safety-critical systems. Existing methods of software development and systems based on ML are fundamentally different. Moreover, the existing certification methods for automotive systems cannot fully certify the safe operation of ML-based components and subsystems. This is because existing safety certification criteria were formulated before the advent of ML. Therefore, new or adapted methods are needed to certify ML-based systems. This article analyses the existing safety standard, ISO26262, for automotive applications, to determine the certifiability of ML approaches used in low-risk automotive applications. This will contribute towards addressing the task of assuring the security and safety of ML-based autonomous driving systems, particularly for low-risk automotive applications, to gain the trust of regulators, certification agencies, and stakeholders.