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Smart Structures and Systems
  Volume 15, Number 3, March 2015 , pages 699-715

A statistical framework with stiffness proportional damage sensitive features for structural health monitoring
Luciana Balsamo, Suparno Mukhopadhyay and Raimondo Betti

    A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) \"Is there a change in the current state of the structure compared to the baseline state?\", (b) \"Does the change indicate a localized stiffness reduction or increase?\", with the latter representing a situation of retrofitting operations, and (c) \"What is the severity of the change in a probabilistic sense?\". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple \"non-damage\" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.
Key Words
    stiffness proportional DSF; empirical complementary CDF; probabilistic damage detection
Luciana Balsamo, Suparno Mukhopadhyay and Raimondo Betti: Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA

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