  Conventional methods for visual assessment of civil infrastructures have
certain limitations, such as subjectivity of the collected data, long
inspection time, and high cost of labor. Although some new technologies i.e.
robotic techniques that are currently in practice can collect objective,
quantified data, the inspectors own expertise is still critical in many
instances since these technologies are not designed to work interactively with
human inspector. This study aims to create a smart, human centered method that
offers significant contributions to infrastructure inspection, maintenance,
management practice, and safety for the bridge owners. By developing a smart
Mixed Reality framework, which can be integrated into a wearable holographic
headset device, a bridge inspector, for example, can automatically analyze a
certain defect such as a crack that he or she sees on an element, display its
dimension information in real-time along with the condition state. Such systems
can potentially decrease the time and cost of infrastructure inspections by
accelerating essential tasks of the inspector such as defect measurement,
condition assessment and data processing to management systems. The human
centered artificial intelligence will help the inspector collect more
quantified and objective data while incorporating inspectors professional
judgement. This study explains in detail the described system and related
methodologies of implementing attention guided semi supervised deep learning
into mixed reality technology, which interacts with the human inspector during
assessment. Thereby, the inspector and the AI will collaborate or communicate
for improved visual inspection.
