Automated piecewise linear regression for analyzing structural health monitoring data

H. Garg, K. Yang , A. G. Cohn, D. Borman, S. V. Nanukuttan, P. A. M. Basheer

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)93-104
JournalACI Materials Journal
Issue number2
Publication statusPublished - 01 Mar 2024


  • artificial intelligence
  • automated clustering-based piecewise linear regression
  • diffusion coefficient
  • electrical resistance
  • in-service performance
  • structural health monitoring


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