Applying the Continuous Hidden Markov Model to Structural State Estimation
Publication: Practice Periodical on Structural Design and Construction
Volume 28, Issue 2
Abstract
Assessing large structures on the basis of multivariate static data sets is a challenging task for a number of reasons. One is that a low-frequency sample does not consistently capture longitudinal discrepancies in the data and thus cannot translate multivariate information into structural conditions and performance metrics. Another is the fluctuation in the data due to the synergy between loading and environmental factors. The precise elimination of these factors is difficult and can lead to inaccuracies in a structural condition assessment. In this study, an advanced machine learning framework employing the Continuous Hidden Markov Model (CHMM) is proposed to address this challenge using multivariate static data flows. Trained CHMMs are used to determine the probabilistic states of given degrees of freedom (e.g., a member or element within the structure) with respect to the input data. To build reliable training sets, two key factors need to be accounted for: (1) the numerical modeling errors inherent in training data, and (2) the sensitivity of the data to localized damage (e.g., the decrement of stiffness in a component). To handle Factor (1), an inversion-based regime is adopted to account for modeling errors by modifying the global (finite element) stiffness matrix. For Factor (2), the use of amplifying functions effectively increases the distinguishability of data and CHMMs to state changes. The efficacy of the CHMM-based framework is numerically demonstrated on a simple support beam and then on a cable-stayed bridge section. In both cases, the results demonstrate that the proposed method can capture the damage location and detect damage extent when the noise is below an acceptable level.
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Data Availability Statement
Some models and code generated or used during the study are available from the corresponding author by request. These may include simulation data and code used to generate models used in this work.
Acknowledgments
LL, IH, KP, and DS would like to acknowledge the support of the Department of Civil and Structural Engineering at the University of Sheffield.
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Published In
Practice Periodical on Structural Design and Construction
Volume 28 • Issue 2 • May 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 14, 2022
Accepted: Oct 4, 2022
Published online: Dec 19, 2022
Published in print: May 1, 2023
Discussion open until: May 19, 2023
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