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The integration of Artificial Intelligence (AI) in collaborative tasks has gained momentum, with particular implications for critical infrastructure maintenance. This study examines the assurance goals of AI—security, explainability, and trustworthiness—within Virtual Reality (VR) environments for bridge maintenance. Adopting a within-subjects design approach, this research leverages VR environments to simulate real-world bridge maintenance scenarios and gauge user interactions with AI tools. With the industry transitioning from paper-based to digital bridge maintenance, this investigation underscores the imperative roles of security and trust in adopting AI-assisted methodologies. Recent advancements in AI assurance within critical infrastructure highlight its monumental role in ensuring safe, explainable, and trustworthy AI-driven solutions. / Master of Science / In today's rapidly advancing world, the traditional methods of inspecting and maintaining our bridges are being revolutionized by digital technology and artificial intelligence (AI). This study delves into the emerging role of AI in bridge maintenance, a field historically reliant on manual inspection. With the implementation of AI, we aim to enhance the efficiency and accuracy of assessments, ensuring that our bridges remain safe and functional. Our research employs virtual reality (VR) to create a realistic setting for examining how users interact with AI during bridge inspections. This immersive approach allows us to observe the decision-making process in a controlled environment that closely mimics real-life scenarios. By doing so, we can understand the potential benefits and challenges of incorporating AI into maintenance routines. One of the critical challenges we face is the balance of trust in AI. Too little trust could undermine the effectiveness of AI assistance, while too much could lead to overreliance and potential biases. Furthermore, the use of digital systems introduces the risk of cyber threats, which could compromise the security and reliability of the inspection data. Our research also investigates the impact of AI-generated explanations on users' decisions. In essence, we explore whether providing rationale behind AI's recommendations helps users make better judgments during inspections. The ultimate objective is to develop AI tools that are not only advanced but also understandable and reliable for those who use them, even if they do not have a deep background in technology. As we integrate AI into bridge inspections, it's vital to ensure that such systems are protected against cyber threats and that they function as reliable companions to human inspectors. This study seeks to pave the way for AI to become a trusted ally in maintaining the safety and integrity of our infrastructure.

With the majority of bridges in the country aging, over capacity and costly to rehabilitate or replace, it is essential that engineers refine their inspection and evaluation techniques. Over the past 130 years the information gathering techniques and methods used by engineers to inspect bridges have changed little. All of the available methods rely on one technique, visual inspection. In addition, over the past 40 years individual bridge inspectors have gone from being information gathers to being solely responsible for the condition rating of bridges they inspect. The reliance on the visual abilities of a single individual to determine the health of a particular bridge has led to inconsistent and sometimes erroneous results. In an effort to provide bridge inspectors and engineers with more reliable inspection and evaluation techniques, this thesis will detail the case for development of a new inspection tool, and the assembly and use of one new tool called Fringe Interferometry

<p>In recent years, there have been a number of truck fires involving bridges with concrete components. If the fire burns for a significant period of time, the structural integrity of concrete components could be damaged due to fire. Research-based guidance for evaluating the level of fire damage is currently unavailable and would be beneficial for post-fire bridge inspectors. </p> <p>This research project focused on evaluating the effects of fire induced damage on concrete bridge deck elements. In order to achieve this goal, a series of controlled heating experiments and material analysis were conducted. Two concrete bridge deck specimens from the I-469 bridge over Feighner Road were heated for different time durations (40 - 80 min.) following the ISO-834 temperature-time curve. The deck specimens were cooled naturally after the specific heating durations. The temperature profiles through the depth of deck specimens were measured during heating and cooling. After testing, concrete samples were taken from the deck specimens for material analysis. Different types of material tests were conducted on samples taken from the undamaged and damaged deck specimens. The material test results were used to evaluate the effects of fire induced damage on the concrete microstructure, and to correlate the microstructure degradation with the through-depth temperature profiles of deck specimens. </p> <p>From the experimental results, several critical parameters that can affected by fire temperature and duration were discussed: (i) through-depth temperature profiles of deck specimens, (ii) cracks on the exposed surface of deck specimens, (iii) color changes of deck specimens, (iv) microstructure of heated concrete samples, (v) content of calcium hydroxide in fire damaged concrete samples at various depths. Based on the results from heating experiments and observations from material analysis, recommendations and guidance for evaluating concrete decks subjected to realistic fire scenarios are provided to assist bridge inspectors.</p>

Bridges are a vitally important part of modern infrastructure. Their condition needs to be monitored on a continuous basis in order to ensure their safety and functionality. Teams of engineers visually inspect more than half a million bridges per year in the US and the EU. There is clear evidence to suggest that they are not able to meet all bridge inspection guideline requirements. In addition, the format and storage of inspection reports varies considerably across authorities because of the lack of standardisation. The availability of a comprehensive and open digital representation of the data involved in and required for bridge inspection is an indispensable necessity for exploiting the full potential of modern digital technologies like big data exploration, artificial intelligence and database technologies. A thorough understanding of bridge inspection information requirements for reinforced concrete bridges is needed as basis for overcoming the stated problem. This work starts with a bridge inspection guideline analysis, from which an information model and a candidate binding to Industry Foundation Classes (IFC) is developed. The resulting bridge model can fully store inspection information in a standardised way which makes it easily shareable and comparable between users and standards. Then, two inspection stages for locating and classifying visual concrete defects are devised, implemented and benchmarked to support the bridge inspection process: In a first stage, healthy concrete surfaces are located and disregarded for further inspection. In a second hierarchical classification stage, each of the remaining potentially unhealthy surface areas is classified into a specific defect type in accordance with bridge inspection guidelines. The first stage achieves a search space reduction for a subsequent defect type classification of over 90% with a risk of missing a defect patch of less than 10%. The second stage identifies the correct defect type to a potentially unhealthy surface area with a probability of 85%. A prototypical implementation serves as a proof of concept. This work closes the gap between requirements arising from established inspection guidelines, the demand for holistic data models which has recently become known as "digital twin", and methods for automatically identifying and measuring specific defect classes on small scale images. It is of great significance for bridge inspectors, bridge owners and authorities as they now have more suitable data models at hand to store, view and manage maintenance information on bridges including defect location and defect types which are being retrieved automatically. With these developments, a foundation is available for a complete revision of bridge inspection processes on a modern, digital basis.

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The thesis Building Investigation and Diagnosis of Reinforced Concrete Structure is focused on analyzing two reinforced concrete bridge structures. It discusses different evaluation surveys and its influence on later evaluation of the bridges. In this thesis there is included static evaluation finding supports at one of reinforced concrete bridges. Practical part is complemented by the necessary theoretical part, which deals with exploring bridges, ways of their implementation, and a summary of the most commonly used diagnostic methods for bridges.

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<p>  </p> <p>For the agencies who are maintaining the transportation infrastructure, staying up to date with inspections is a continuing challenge. One approach to addressing that is to allow an inspector to perform most of the inspection process by viewing a digital 3D model, which is accurate and substantially complete. Having a digital 3D model could limit the on-site inspection process to those cases where the virtual inspection suggests more input is necessary. Such models would be defined by point clouds or by a surface composed of textured polygons. One of the advantages of building the 3D model via textured polygons instead of point clouds is that the inspector can zoom in and see the detail as needed. The data required to construct such a model are photographs that can be captured by a combination of handheld cameras and unmanned aerial vehicles (UAV). Having such a model will help these agencies to improve the efficiency of their inspection process in several ways, such as lowering the overall inspection costs, fewer lane closures during the inspection procedures, and having digital archives for their infrastructure. Of course, the time and effort to collect the images and build the model are substantial, but once a model is constructed, subsequent images can be applied as texture without recreating the model.</p> <p>This research will cover the task of building an accurate 3D wireframe model for a bridge that can be used to display texture realistically via rigorous image projection onto the wireframe surface. The wireframe geometry will be substantially derived from extracted linear features. The model’s estimation process will integrate the photogrammetric bundle block adjustment technique with suitable methods to estimate the linear feature parameters. Prior to the developments above, an investigation has been done to determine the possibility of automating the process of selecting the conjugate points using <em>Structure-From-Motion</em> (SFM) algorithms, as implemented in programs such as <em>AGISOFT or PIX4D</em>. </p> <p>In this kind of application, the bridge mostly has two types of linear features: the Straight Linear Features (SLF), which can be found on the component elements of the bridge structure, and the Parabolic Linear Features (PLF) for linear elements spanning the entire bridge length. After estimating the parameters of the linear features, the quadrilateral polygons used in the wireframe/visualization process can be extracted using these parameters. Furthermore, these quadrilateral polygons form the foundation for image texture projection. Also noteworthy, the process of generating these quadrilateral polygons is substantially automated.</p> <p>Whenever doing least squares estimation, one needs a way to express the uncertainty of the computed parameters (unknowns). In the early stages of the project, one may not know the uncertainty of the observations. Often pairs of parameters (typically X, Y position) need their uncertainties to be displayed together, graphically, in the form of a confidence circle with a given probability. Under these conditions, the literature offers no guidance on how it should be constructed rigorously. This research develops such a technique. In geomatics, there are two cases when making confidence statements. The first one is when the observation uncertainties are known. If the case is 1D, the corresponding probability density function is the univariate normal distribution. When the case is 2D, the chi-squared distribution will be used for the elliptical region, and the multivariate normal distribution will be used when making confidence circles. The second condition is when the uncertainties of the observations are unknown. When these uncertainties are unknown, the univariate t-distribution will be used to make the 1D confidence statement. The F-distribution will be used for the elliptical region. For a confidence circle, the multivariate t-distribution must be used. This research will present an algorithm to implement this process and show, numerically, that it is valid. </p>