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Damage and repair classification in reinforced concrete beams using frequency domain data

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Abstract

This research aims at developing a new vibration-based damage classification technique that can efficiently be applied to a real-time large data. Statistical pattern recognition paradigm is relevant to perform a reliable site-location damage diagnosis system. By adopting such paradigm, the finite element and other inverse models with their intensive computations, corrections and inherent inaccuracies can be avoided. In this research, a two-stage combination between principal component analysis (PCA) and Karhunen–Loéve Transformation (also known as canonical correlation analysis) was proposed as a statistical-based damage classification technique. Vibration measurements from frequency domain were tested as possible damage-sensitive features. The performance of the proposed system was tested and verified on real vibration measurements collected from five laboratory-scale reinforced concrete beams modelled with various ranges of defects. The results of the system helped in distinguishing between normal and damaged patterns in structural vibration data. Most importantly, the system further dissected reasonably each main damage group into subgroups according to their severity of damage. Its efficiency was conclusively proved on data from both frequency response functions and response-only functions. The outcomes of this two-stage system showed a realistic detection and classification and outperform results from the PCA-only. The success of this classification model is substantially tenable because the observed clusters come from well-controlled and known state conditions.

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Authors and Affiliations

  1. Civil Engineering Department, Al-Mustansiriya University, Baghdad, Iraq

    Ali A. Al-Ghalib

  2. School of Architecture, Design and the Built Environment, Nottingham Trent University, Nottingham, NG1 4BU, UK

    Fouad A. Mohammad

Authors
  1. Ali A. Al-Ghalib
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  2. Fouad A. Mohammad
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Correspondence to Ali A. Al-Ghalib.

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Al-Ghalib, A.A., Mohammad, F.A. Damage and repair classification in reinforced concrete beams using frequency domain data. Mater Struct 49, 1893–1903 (2016). https://doi.org/10.1617/s11527-015-0621-7

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  • DOI: https://doi.org/10.1617/s11527-015-0621-7

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