Vibration monitoring of structures with indirect load identification and Kalman update

Abstract

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, due to the continuously increasing amount of old infrastructure buildings. 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 to create a kind of functional digital twin for SHM. For this purpose, a sound numerical model and a measurement system with a continuous data flow are introduced to provide online predictions of the state and response parameters of the structure. System changes resulting from damage or aging processes can be detected and localized, provided the measurement and the model prediction deal with the same cause. Thus, the load identification is a necessary prerequisite for reliable data assimilation techniques. A two-step update procedure is proposed and applied in this context. At first, one part of the measurement system is used for the load identification. A cluster structure of convolutional neural networks (CNNs) was developed, trained and calibrated to extract load characteristics such as load magnitudes, load velocities or the number of vehicles on the bridge from multiple acceleration signals. This information is then used to reconstruct the actual load. In the second step, a different set of sensors is used for the data assimilation. In contrast to the first set of measurement locations, the measurement data from these sensors should be sensitive to potential system changes or damage. Here, the identified load is used as input for the model predictions which are then compared to the measurement data. A combination of different ensemble based Kalman filters (KF) provides a sequential update of the state parameters (e.g. displacement, velocity, acceleration) and the model parameters (e.g. stiffness, mass, damping). The cluster CNN approach is tested numerically, and the data assimilation technique is tested on a laboratory structure.