Transforming bridge inspections through digital technologies

Inspection and maintenance are essential to ensure the serviceability and safety of bridges. Current inspection on site is often done manually on paper, and paper is the medium to exchange condition information between the involved stakeholders. After damage registration or information exchange, the data is processed digitally. The repeated digitalization is an error-prone process and leads to redundant work. To address these shortcomings, this research aims to devise a framework and deliver an augmented reality toolkit for identification and classification of damage in bridges using deep learning-based sematic segmentation techniques. To achieve this goal, the following steps will be carried out: i) data acquisition for selected bridge case studies; ii) data processing and filtering; iii) image segmentation using deep learning; iv) based on the segmented images, classification of damage type and severity by code cross-checking; v) development of a user-friendly augmented reality toolbox for on-site damage identification and classification based on the trained data set. This technology will enable linking the reconstructed bridge condition with a digitized BIM model and to support the complete lifecycle of built infrastructure. Finally, the expert toolkit will embed expert knowledge for decision making support related to repair and retrofitting demands and alternatives for the diagnosed bridge condition.