Garg, H., Yang, K., Cohn, A.G. orcid.org/0000-0002-7652-8907 et al. (3 more authors) (2024) Automated Piecewise Linear Regression for Analyzing Structural Health Monitoring Data. ACI Materials Journal, 121 (2). pp. 93-104. ISSN 0889-325X
Abstract
The recent increased interest in structural health monitoring (SHM) related to material performance has necessitated the application of advanced data analysis techniques for interpreting the realtime data in decision-making. Currently, an accurate and efficient approach for the timely analyses of large volumes of uncertain sensor data is not well-established. This paper proposes an automated clustering-based piecewise linear regression (ACPLR)-SHM methodology for handling, smoothing, and processing large data sets. It comprises two main stages, where the gaussian weighted moving average (GWMA) filter is used to smooth noisy data obtained from electrical resistance sensors, and piecewise linear regression (PLR) predicts material properties for assessing the performance of concrete in service. The obtained values of stabilized resistance and derived values of diffusion coefficients using this methodology have clearly demonstrated the benefit of applying ACPLR to the sensor data, thereby classifying the performance of different types of concrete in service environments.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024, American Concrete Institute. This is an author produced version of an article accepted for publication in ACI Materials Journal. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Civil Engineering; Engineering; Materials Engineering |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) > Institute for Resilient Infrastructure (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jun 2024 15:45 |
Last Modified: | 25 Jun 2024 15:45 |
Status: | Published |
Publisher: | American Concrete Institute |
Identification Number: | 10.14359/51740370 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213913 |