Towards Damage Prediction: Mapping Inspection History of Concrete Bridges

Abstract: The analog nature of bridge inspections hinders long term prognosis of structural health. Accordingly, we propose a comprehensive digitization concept to enhance the effectiveness of bridge inspections: historical inspection data is consolidated into digital damage models with machine learning. These models can then be combined with a BIM model for the creation of a digital twin, which paves the way for an evaluation of damage progression. The model furthermore enables inspectors to easily locate existing damages via images and point clouds, and to record changes into a new damage model. This significantly improves maintenance and retrofitting efforts.