Bridge Cracks Monitoring: Detection, Measurement, and Comparison Using Augmented Reality [supporting datasets]

University Transportation Centers Program2021XLSXDatasetMoreu, FernandoMalek, KavehTransportation Consortium of South-Central States (Tran-SET) University Transportation Center for Region 6Louisiana State University (Baton Rouge, La.). University Transportation Center for Region 6United States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyUnited States. Department of Transportation. University Transportation Centers (UTC) ProgramTransportation Consortium of South-Central StatesUnited StatesBridgesCamerasCrackingData collectionField studiesInspectionInspection equipmentStructural health monitoringPattern recognition systemsImage processingAugmented RealityCanny Algorithm20STUNM3369A3551747106Crack occurrence and propagation are among critical factors that affect the performance and lifespan of civil infrastructures such as bridges. Consequently, numerous crack detection and measurement methods have been proposed and developed in the recent decades in the areas of Structural Health Monitoring and non-destructive testing. Many novel technologies have emerged with the potential to overcome the limitations of the presented techniques of crack detection and characterization. Crack detection and characterization method used in this research lies in supplementing human visual inspection capabilities in a systematic manner through an appropriate level of automation. The Augmented Reality (AR) tool developed in this project allows a user to perform tasks in a real-world environment while visually receiving supplementary 3D computer-generated information to support the tasks. More specifically, we developed a crack detection/characterization tool in this research and deployed it in Microsoft HoloLens smart glasses. This AR tool provides the user with automatic data collection capability through AR headset camera and is a means of hands-free data sharing for inspectors while conducting their normal inspection. We conducted several laboratory and field experiments by which we evaluated the effectiveness of the developed crack detection and measurement system. The result confirm that the AR tool devised in this project has the potential to help the inspection process in terms of time, comfort and accuracy.132