Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification

TL;DR

This paper compares deep recurrent-convolutional neural networks with physics-based Kalman filtering for dynamic load identification in structures, highlighting their strengths and limitations across various scenarios.

Contribution

It provides a comparative analysis of neural networks and residual Kalman filters for structural load identification under realistic and challenging conditions.

Findings

Neural networks outperform Kalman filter in some scenarios with limited data.

Kalman filter performs better in physically identifiable cases.

Different methods excel under different loading conditions.

Abstract

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.