Abstract Summary
In 2021, the road network in Germany included approximately 40,000 bridges of which most had been constructed between 1960 and 1985. Thus, most bridges have reached a critical age which makes the lifecycle management crucial and gives structural health monitoring (SHM) great significance. Unfortunately, SHM methods are still rather matter of research than part of technical regulations. Therefore, the German Research Foundation (DFG) has launched in September 2022 the priority program SPP 2388 100+ to develop new methods for digital representation, SHM and lifetime management of complex structures suitable for engineering practice. The present contribution is prepared within the LEMOTRA project as a part of SPP 2388 100+. Among various SHM methods, the approach based on the Kalman update for data assimilation between model and measurement is applied and further developed. A sound numerical model, the measurement system, a permanent data flow and assimilation shall provide an online prediction of the state and response parameters of the structure. The whole system could be considered as a kind of functional digital twin for SHM. A two-step update procedure is proposed and applied in this context. Various Kalman filters have already been used for state updates in structural dynamics. They require, however, the knowledge of the actual loading for robust predictions. As an alternative, white noise input functions have been applied that cannot generally guarantee reliable results for real structures. Therefore, a part of the measurement system is used to identify the loading itself in the first step. The choice of the sensor types, locations and measurement directions is based on two requirements. The measured values need to correlate well with the loading and are less influenced by potential system changes or damage. As a result, a suitable correlation between the measured values and the loading history can be defined, calibrated and used in the Kalman filter. In the second step, a different set of sensors is used for the data assimilation process. Here, the identified load from the first step is used as input for the model prediction at any given point of time which is then compared to the measurement data of the second set of sensors. The state parameters (e.g. displacement, velocity, acceleration) and the model parameters (e.g. stiffness, mass, damping) are then sequentially updated by a combination of different ensemble based Kalman filters. The proposed approach is implemented in MATLAB and tested on laboratory structures under various loading and damage scenarios. At that, the advantages and drawbacks of the approach are discussed.
