Highway bridge crack inspection verification and training using an automated augmented reality system

Rising traffic volumes and natural disasters such as earthquakes and typhoons accelerate bridge deterioration and intensify public concern over aging infrastructure. With aging bridges continuing to operate at peak capacity, there is an urgent need for an efficient, consistent, and reliable method of bridge-damage inspection. The DER and U (Degree of degradation, Extension, Relevance, and Urgency) visual inspection method, currently widely used in bridge inspections, is significantly influenced by high inspector turnover rates and inspector subjectivity, leading to inconsistencies in inspection results and recommendations. To address this need, an augmented reality (AR)-based mobile application was developed in this study to enhance bridge inspection training and improve data management. Using Google ARCore's plane identification technology, the developed application accurately overlays virtual deterioration models onto real-world surfaces, providing inspectors with data that may be applied directly to objective decision making. The developed AR tool not only boosts trainee engagement and understanding but also includes a robust image collection and storage feature that enables inspectors to systematically capture, classify, and archive bridge condition images. This data repository supports the consistent, long-term tracking of structural deterioration and facilitates degradation-pattern analysis, ultimately contributing to more-effective bridge infrastructure maintenance planning and lifecycle management. The results of this study suggest the developed AR-based application has strong potential to modernize bridge inspection processes, offering the dual benefits of enhanced training and reliable data collection.