5 papers
An unscented Kalman filter method for real time input-parameter-state estimation
Marios Impraimakis, Andrew W. Smyth
The input-parameter-state estimation capabilities of a novel unscented Kalman filter is examined herein on both linear and nonlinear systems. The unknown input is estimated in two…
A Kullback-Leibler divergence method for input-system-state identification
Marios Impraimakis
The capability of a novel Kullback-Leibler divergence method is examined herein within the Kalman filter framework to select the input-parameter-state estimation execution with the…
A convolutional neural network deep learning method for model class selection
Marios Impraimakis
The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the resp…
Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification
Marios Impraimakis
The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination…
A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation
Marios Impraimakis, Evangelia Nektaria Palkanoglou
The optimization-based damage detection and damage state digital twinning capabilities are examined here of a novel conditional-labeled generative adversarial network methodology.…