Model Free Interpretation of Monitoring Data

  • Daniele Posenato
  • Francesca Lanata
  • Daniele Inaudi
  • Ian F. C. Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4200)


No current methodology for detection of anomalous behavior from continuous measurement data can be reliably applied to complex structures in practical situations. This paper summarizes two methodologies for model-free data interpretation to identify and localize anomalous behavior in civil engineering structures. Two statistical methods i) moving principal component analysis and ii) moving correlation analysis have been demonstrated to be useful for damage detection during continuous static monitoring of civil structures. The algorithms memorize characteristics of time series generated by sensor data during a period called the initialisation phase where the structure is assumed to behave normally. This phase subsequently helps identify anomalous behavior. No explicit (and costly) knowledge of structural characteristics such as geometry and models of behaviour is necessary. The methodologies have been tested on numerically simulated elements with sensors at a range of damage severities. A comparative study with wavelets and other statistical analyses demonstrates superior performance for identifying the presence of damage.


Anomalous Behavior Damage Detection Structural Health Monitoring Civil Structure Civil Engineering Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniele Posenato
    • 1
  • Francesca Lanata
    • 2
  • Daniele Inaudi
    • 1
  • Ian F. C. Smith
    • 3
  1. 1.Smartec SAMannoSwitzerland
  2. 2.Department of Structural and Geotechnical EngineeringUniversity of GenoaGenoaItaly
  3. 3.Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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