Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification
Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification
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.
Gated recurrent unit (GRU), long short-term memory (LSTM), Artificial neural networks (ANNs), one-dimensional convolutional networks (1D-CNNs), machine learning (ML), Artifical Intelligence (AI), Kalman filter (KF), structural health monitoring (SHM), Structural Dynamics, Earthquake Engineering, system identification, load identification, force identification, Input estimation, parameter estimation, state estimation, Bayesian inference update, IASC-ASCE, San Bernandino, Residual-based Kalman filter, Deep learning (DL), Experimantal Validation
Impraimakis, Marios
e8a4540d-2348-4422-9d13-d335b128b02b
Impraimakis, Marios
e8a4540d-2348-4422-9d13-d335b128b02b
Impraimakis, Marios
(2024)
Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification.
Structural Health Monitoring.
(doi:10.1177/14759217241262972).
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.
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impraimakis-2024-deep-recurrent-convolutional-neural-network-learning-and-physics-kalman-filtering-comparison-in
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e-pub ahead of print date: 27 July 2024
Keywords:
Gated recurrent unit (GRU), long short-term memory (LSTM), Artificial neural networks (ANNs), one-dimensional convolutional networks (1D-CNNs), machine learning (ML), Artifical Intelligence (AI), Kalman filter (KF), structural health monitoring (SHM), Structural Dynamics, Earthquake Engineering, system identification, load identification, force identification, Input estimation, parameter estimation, state estimation, Bayesian inference update, IASC-ASCE, San Bernandino, Residual-based Kalman filter, Deep learning (DL), Experimantal Validation
Identifiers
Local EPrints ID: 493140
URI: http://eprints.soton.ac.uk/id/eprint/493140
ISSN: 1475-9217
PURE UUID: 5a332153-e21b-431a-8041-cc52ecb15197
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Date deposited: 23 Aug 2024 16:52
Last modified: 23 Aug 2024 16:52
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Author:
Marios Impraimakis
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