Human Computer Interaction Design for Assisted Bridge Inspections via Augmented Reality

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Virginia Tech


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.



Bridge Inspection, Infrastructure Assessment, Augmented Reality, Human in the Loop Automation, Adaptable Automation