Permanent Structural Health Monitoring of a new prestressed concrete bridge

Abstract

Abstract The digitization of processes and methods has been in the focus of construction industry in recent years. Whereas in the automotive industry predictive maintenance is state of the art, the prediction of the service lifetime for bridges is not standardized due to missing sufficient data to develop and train machine learning algorithms. Structural Health Monitoring (SHM) for bridges is used, e.g., to monitor a local damage trend or a structure's response to external influence. Because of the typical SHM tasks, sensors are often built to be attached temporarily to the structure and to be removed afterwards. For predictive maintenance, there is a demand for data from the beginning of the lifetime. In this paper we show challenges and solutions to implement sensors during construction like the integration in the construction planning, the geo‐localization of the sensors, and durability of the sensors to obtain data from first day of lifetime. All these aspects are shown in a practical example for the newly built Isen bridge in Schwindegg, Germany, where a sensory SHM‐system has been installed to measure the bridge's condition starting from day one of its service lifetime. The results show that with a flexible installation team, the installation does not lead to delay in construction.