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 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.