To address some of the challenges associated with aging bridge infrastructure, this dissertation explores the development and evaluation of a novel tool for bridge inspections leveraging Augmented Reality (AR) and computer vision (CV) technologies to facilitate measurements. Named the Wearable Inspection Report Management System (WIRMS), the system supports various data entry methods and an adaptable automation workflow for defect measurements, showcasing AR's potential to improve bridge inspection efficiency and accuracy. Within this context, the work's main research goal is to understand the difference in performance between traditional field data collection methods (i.e. pen and paper) and automated methods like spoken data entry and CV-based structural defect measurements. In case of CV assistance, emphasis was placed on human-computer interaction (HCI) to understand whether partial, collaborative automation could address some of the limitations of fully automated inspection methods. The project began with comprehensive data collection through interviews, surveys, and observations at bridge sites, which informed the creation of a Virtual Reality (VR) prototype. An initial user study tested the feasibility of using voice commands for data entry in the AR environment but found it impractical. A second user study focused on optimizing interaction methods for virtual concrete crack measurements by testing different degrees of automated CV assistance. As part of this effort, major technical contributions were made to back-end technologies and CV algorithms to improve human-machine collaboration and ensure the accuracy of measurements. Results were mixed, with larger degrees of automation resulting in significant reductions in inspection time and perceived workload, but also significant increases in the amount of measurement error. The latter result is strongly associated with a lack of field robustness of CV methods, which can under-perform if conditions are not ideal. In general, hybrid techniques which allow the user to correct CV results were seen as the most favorable. Field validations with bridge inspectors showed promising potential for practical field implementation, though further refinement is needed for broader deployment. Overall, the research establishes a viable path for making AR a central component to future inspection practices, including digital data collection, automation, data analytics, and other technologies currently in development. / Doctor of Philosophy / This dissertation investigates the development of an innovative tool designed to transform bridge inspections using Augmented Reality (AR) technology, incorporating advanced computer vision (CV) techniques to assist with measurements. The project began with thorough data collection, including interviews and observational studies at bridge sites, which directly influenced the tool's design. A prototype was initially created in a Virtual Reality (VR) environment to refine the functionalities needed for AR application. The resulting AR system supports various interactive methods for documenting and measuring bridge defects, showcasing how AR can streamline and enhance traditional bridge inspection processes. However, challenges remain, particularly in accurately measuring certain types of defects, indicating that some traditional tools are still necessary. Despite these challenges, early tests with bridge inspectors have been promising, suggesting that AR could significantly improve the efficiency and accuracy of bridge inspections. The research demonstrates a clear path forward for further development, with the potential to revolutionize how bridge inspections are conducted.

As Artificial Intelligence (AI) and automation become more intertwined, understanding their impact on user control is essential. This study investigates dynamic user control in AI-driven automated systems, particularly in work environments. While adaptive automation (AA) has been extensively studied, there's a gap in research on adaptable and hybrid automation, where users control the level of automation (LOA). We bridge this gap with a design-oriented case study structured into three phases, evaluating one adaptable and one hybrid prototype. By understanding real-world perspectives of users and providers of an AI-driven automation system, we address the question: "How can we support users with dynamic control when designing for human-centred automation?”. Our findings are synthesized into insights that suggest a preference for a hybrid approach; one that balances user and AI-system collaboration, providing adaptive and personalized support, without overwhelming adaptability. Overall, our results conclude the importance of human involvement in the automation process, underscoring the need for "human touch” in the design of humancentred automation (HCAI).