Damage identification for structural health monitoring using fuzzy pattern recognition

Uncertainty abounds with in situ structural performance assessment and damage detection in Structural Health Monitoring (SHM). Most research in SHM focuses on statistical analysis, data acquisition, feature extraction and data reduction. We introduce a method to improve pattern recognition and damag...

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Bibliographic Details
Published in:Engineering structures 2005-10, Vol.27 (12), p.1774-1783
Main Authors: Reda Taha, M.M., Lucero, J.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial neural network
Bayesian updating
Building failures (cracks, physical changes, etc.)
Buildings. Public works
Computation methods. Tables. Charts
Damage index
Durability. Pathology. Repairing. Maintenance
Exact sciences and technology
Fuzzy set
Structural analysis. Stresses
Structural health monitoring
Wavelet multi-resolution analysis
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Summary:Uncertainty abounds with in situ structural performance assessment and damage detection in Structural Health Monitoring (SHM). Most research in SHM focuses on statistical analysis, data acquisition, feature extraction and data reduction. We introduce a method to improve pattern recognition and damage detection by supplementing Intelligent Structural Health Monitoring (ISHM) with fuzzy sets. Intuitively we know that damage does not occur as a Boolean relation (one of two values, true or false) but progressively. Bayesian updating is used to demarcate levels of damage into fuzzy sets accommodating the uncertainty associated with the ambiguous damage states. The new techniques are examined to provide damage identification using data simulated from finite element analysis of a prestressed concrete bridge without a priori known levels of damage.
ISSN:0141-0296
1873-7323
DOI:10.1016/j.engstruct.2005.04.018