Automated Piecewise Linear Regression for Analyzing Structural Health Monitoring Data

Harshita Garg, K. Yang, A. G. Cohn, Duncan 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
Number of pages12
JournalACI Materials Journal
Issue number2
Publication statusPublished - Apr 2024


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

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • General Materials Science


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