Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification
This addresses uncertainty in load prediction for civil engineering applications with limited test data or unidentifiable structural models.
This paper compares deep learning methods (GRU, LSTM, CNN) with a physics-based residual Kalman filter for dynamic structural load identification, finding that each method outperforms the others in different loading scenarios, with the RKF performing best in physically parametrized identifiable cases.
The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination is on realistic small dataset training conditions and on a comparative view to the physics-based residual Kalman filter (RKF). The dynamic load identification suffers from the uncertainty related to obtaining poor predictions when in civil engineering applications only a low number of tests are performed or are available, or when the structural model is unidentifiable. In considering the methods, first, a simulated structure is investigated under a shaker excitation at the top floor. Second, a building in California is investigated under seismic base excitation, which results in loading for all degrees of freedom. Finally, the International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring benchmark problem is examined for impact and instant loading conditions. Importantly, the methods are shown to outperform each other on different loading scenarios, while the RKF is shown to outperform the networks in physically parametrized identifiable cases.