Applying automated damage classification during digital inspection of structures

Facing a continually growing stock of buildings reaching critical ages considering the occurrence of damage, the inspection of built structures is more important than ever. Due to staff shortages and limited financial resources, authorities may fail to ensure essential frequent inspections. Therefore, many companies and research institutions have recently started to develop concepts for a digital structural inspection which decisively facilitates and accelerates the current analogue process. Digitalized inspections include the generation of a digital twin composed of a building information model as well as the recognized, measured and assessed damage of the building. Recognition thereby depicts the classification and localization which is made possible by the deployment of convolutional neural networks (CNNs). Recognizing damage is therefore regarded as a crucial component in assessing a specific area’s as well as the building’s overall condition. This paper describes a process for the digital inspection of bridges with mobile devices. One of the concept’s key components is the automated damage recognition. Hence, also the development of CNNs for multi-target classification of damage on reinforced concrete bridges is examined. Finally, the development and usage of an application for live image classification of damage is presented which demonstrates the practical use of CNNs in a real world environment and enables their evaluation.